Search

Arctic Oscillation (AO) is the dominant mode of atmospheric circulation in the extratropical Northern Hemisphere regions. The spatial pattern of the AO Arctic center affects the extent of polar cold air outbreaks southward. However, the underlying nature and causes of its interdecadal variation remain unclear. Utilizing ERA5 reanalysis data, this study identifies two distinct spatial patterns of the wintertime AO Arctic center through the K‐means clustering method, which alternate over different decade periods. The Double‐trough pattern generates a tripolar temperature pattern of "cold Arctic‐warm Eurasia‐cold Tibetan Plateau" through a Rossby wave train during 1960–1997/2013–2024. While the Single‐trough pattern leads to a dipolar temperature pattern in 1998–2012. Furthermore, interdecadal variations in North Atlantic sea surface temperature meridional gradient act as an influencing factor in shaping the spatial pattern of the AO Arctic center. This research aids the understanding and prediction of climate anomalies using AO signals within various decadal contexts. Plain Language Summary: Arctic Oscillation (AO) is a key extratropical atmospheric variability mode in the Northern Hemisphere, significantly influencing weather and climate anomalies in the mid‐latitudes. The shape of AO's trough in the Arctic center governs the southward advancement of Arctic cold air. Using the K‐means clustering method, the spatial patterns of the wintertime AO Arctic center are primarily divided into two distinct categories, which alternate across different decades. Specifically, from 1960 to 1997 and 2013 to 2024, the AO Arctic center exhibits dual troughs over the Ural Mountains and North America, resulting in a distribution of "cold Arctic‐warm Eurasia‐cold Tibetan Plateau." However, during the period from 1998 to 2012, only the North American trough is present, and its temperature connection with Eurasia weakens. Notably, interdecadal variations in the AO Arctic center are affected by North Atlantic sea surface temperature meridional gradient. This study enhances our understanding of the physical significance of AO and assists us in better utilizing it to predict winter weather and climate anomalies. Key Points: The AO Arctic center shows two spatial patterns alternating between Double‐ and Single‐trough configurations on an interdecadal timescaleThe two spatial patterns of the AO Arctic center are modulated by the interdecadal variations of North Atlantic SST meridional gradientThe Double‐trough pattern of the AO Arctic center can cause a unique tripolar pattern of "cold Arctic‐warm Eurasia‐cold Tibetan Plateau" [ABSTRACT FROM AUTHOR]

Solar power will become the largest renewable energy source, contributing to global carbon neutrality. In addition to the well‐recognized temporal intermittency of solar energy supply, the local energy demand to cope with extreme weathers can further stress the energy grid; both the supply and demand can be greatly influenced by future climate change. Here, we redefine solar drought events by considering supply demand imbalance in solar power. Observation and multi‐model simulations reveal an anthropogenic exacerbation of global solar drought frequency in the past three decades. Moreover, compared to the pathway SSP2‐4.5, the carbon neutrality pathway SSP1‐2.6 can mitigate the increasing frequency and severity of solar droughts by 60% and 63% in the 2090s, respectively. Our study suggests a co‐benefit of solar energy stability and security toward carbon neutrality, especially in developing nations where the local supply demand imbalance is a major issue. Plain Language Summary: Solar power is a key renewable source to support the formidable target of carbon neutrality in the coming decades. However, the supply and demand of solar power are both influenced by future climate change. To our knowledge, no studies have provided an integrated perspective of both the supply and the demand side and the potential imbalance lasting for 3 days due to the more impactful compound events. In this study, we first develop an index (SDI) to describe the dynamic changes of Supply Demand Imbalance relationship in solar power and then quantify the potential risk of solar droughts at the global scale. Based on observation and simulations, we reveal an anthropogenic exacerbation of global solar drought frequency in the past three decades. Furthermore, we project notable regional inequality in the possible energy imbalance and highlight the mitigating effect of the carbon neutrality policy on increasing frequency and severity of solar droughts toward the end of the 21st century. Key Points: The frequency of global solar droughts shows large spatial heterogeneityGHG forcing dominates the increasing solar droughts worldwide in the past three decadesThe increasing frequency and severity of solar droughts can be greatly mitigated by a carbon neutrality policy toward the end of the 21st century [ABSTRACT FROM AUTHOR]

Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub‐seasonal time scales remains a great challenge. Current state‐of‐the‐art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning‐based framework that uses meteorological forecasts as inputs to predict global root‐zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state‐of‐the‐art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub‐seasonal scales. Plain Language Summary: Global flash drought risks are prevailing currently and will continue to be an important concern under climate change. Skillful prediction of global flash drought onset and termination events can provide critical early warning information for agricultural preparations and water resources management. However, current weather forecasting models suffer from large errors in root‐zone soil moisture and flash drought predictions. Here, we develop a machine learning‐based framework that could predict 33% and 24% global flash drought onset and termination events at 7 day lead time, better than the state‐of‐the‐art dynamic model. The machine learning‐based framework can reduce soil moisture prediction errors in dynamic coupled models and thus enhances global flash drought prediction skills. The proposed machine learning forecasting approach may provide scientific advances for flash drought early warning at global scales. Key Points: Machine learning is used for global flash drought prediction up to 2 week lead time33% and 24% global flash drought onset and termination events can be correctly predicted at 7 day lead timeMachine learning improves global flash drought prediction over dynamic model [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm‐resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land‐ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation. Plain Language Summary: Mesoscale convective systems (MCSs) account for more than half of tropical total rainfall and are the primary drivers of extreme rainfall and flooding events. However, understanding how the frequency of these systems will change in a warming climate remains a challenge. This is due to both the incomplete representation of these disturbances in global climate models and a lack of fundamental theory to explain their dynamics. In this study, we employ a global storm resolving model to shed light on the response of tropical MCS occurrences to climate warming. What emerges is a striking contrast: MCSs over tropical land are projected to decrease in frequency, while their occurrence over tropical oceans is anticipated to rise. This difference is linked to the relative changes in convective available potential energy and convective inhibition. The divergent response in MCS occurrences has significant implications for both average and extreme precipitation patterns. As a result, it presents challenges for developing regional climate adaptation and mitigation strategies in the face of a warming climate. Key Points: A global storm resolving model is employed to study the response of global mesoscale convective systems (MCSs) to a warming climateTropical MCS occurrence exhibits divergent responses to increased SST, declining over land while increasing over the oceanThis land‐ocean contrast is attributed to the changes in convective available potential energy and convective inhibition, on MCSs [ABSTRACT FROM AUTHOR]

The North Atlantic storm track plays a critical role in setting the regional weather and climate over Western Europe. In response to anthropogenic emissions, the summer North Atlantic storm track has weakened in recent decades and is projected to continue weakening by the end of this century. In light of growing efforts to mitigate climate change, it is crucial to assess how reversible the CO2‐induced storm track weakening is. Here, I show that under CO2 removal scenarios, the recovery timescale of the storm track is more than double its weakening period. It is found that due to a prolonged high‐latitude warming, postponing the execution of mitigation policies beyond doubling of CO2 concentrations would lead to a centennial‐scale recovery of the storms. Given the impacts of weakening summer storms on weather and extreme events, the delayed recovery of the storms might have broader regional consequences for Western Europe. Plain Language Summary: The summer North Atlantic storm track sets the regional weather and climate over Europe, by transferring heat, moisture and momentum across the North Atlantic basin. Anthropogenic emissions were found to weaken the storm track, which would greatly affect precipitation and hot‐dry extreme events over Europe. It is thus critical to assess the reversibility of the summer North Atlantic storm track weakening under a reduction in greenhouse gases. Here, using targeted numerical simulations, I show that the reversibility time‐scale of the storm track is more than double its weakening time‐scale. Thus, postponing the execution of mitigation strategies increases the reversibility time‐scales of the storm track. Specifically, avoiding centennial‐scale impacts of the weakening storm track can be achieved by executing mitigation strategies prior of reaching doubling of CO2 concentrations. Our analysis reveals that the prolonged recovery of the storm track stems from a prolonged warming of the high latitudes relative to the lower latitudes. Key Points: The reversibility time‐scale of the North Atlantic summer storm track is more than double its weakening time‐scalePostponing mitigation policies beyond a doubling of CO2 levels yields centennial‐scale recovery of the storm trackThe prolonged recovery of the storm track stems from a prolonged warming of the high latitudes relative to the lower latitudes [ABSTRACT FROM AUTHOR]

Accurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability. Plain Language Summary: Heat waves, marked by prolonged periods of extremely high surface air temperature (SAT), are among the most devastating natural disasters. Improving the predictive accuracy and extending the forecast lead times for heat wave events are vital for disaster prevention and mitigation. However, current operational models typically exhibit limited skill in predicting heat waves at subseasonal scales (2–6 weeks ahead). This is particularly true in relation to the densely populated regions of southern East China, for which the operational prediction systems of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts are useful only up to approximately 2 weeks ahead. To address this limitation, we developed a deep learning model using a convolutional neural network approach that can reliably predict SAT anomalies and heat wave events in China with lead times of 10–30 days. Our analysis indicated that successful enhancement of the prediction skill of this model hinges on effectively leveraging the modulating effects of intraseasonal and interannual signals on China's SAT. Rather than focusing solely on specific timescale components, our findings suggest that considering interactions across multiple timescales could enhance subseasonal predictability. Key Points: Summertime surface air temperature (SAT) and heat waves in China are influenced by variabilities across different timescalesA deep learning model skillfully predicted SAT and heat waves for periods extending beyond 20 days by incorporating scale interactionsExplainability analysis linked model skill to modulation of local SAT by large‐scale intraseasonal and interannual signals [ABSTRACT FROM AUTHOR]

Persistent fluctuations in the latitudinal position of the North Atlantic (NATL) jet stream are closely linked to extreme weather anomalies, particularly over Europe. Accurately representing jet stream persistence in climate models is essential for enhancing the reliability of future regional extreme weather projections. This study investigates the summer persistence of the NATL jet stream latitudinal fluctuations and the Summer North Atlantic Oscillation (SNAO) using data from CMIP6 models and ERA5 reanalysis. Our findings reveal that CMIP6 models generally overestimate persistence compared to ERA5 during the historical period. Utilizing the relative vorticity tendency equation, we assess the strength of the eddy‐mean flow feedback and identify a positive relationship between persistence and feedback strength across models. We also found that a large fraction of the intermodel spread in precipitation persistence is associated with differences in the persistence of jet fluctuations. Plain Language Summary: The NATL jet stream plays a crucial role in shaping the weather and climate over the NATL and European regions. The jet continuously fluctuates in latitude and strength and guides the storms along its path. When these fluctuations become unusually persistent, they can lock certain weather patterns in place for extended periods, leading to extreme events such as droughts or floods. In this study, we investigate the persistence of the summer NATL jet stream latitudinal fluctuations and that of the SNAO using models and the ERA5 reanalysis. We found that the strength of the eddy‐mean flow feedback explains a large fraction of the intermodel differences in jet persistence. Moreover, we found that differences in jet persistence are closely associated with differences in precipitation persistence over Europe. Key Points: CMIP6 models tend to overestimate the persistence of summer jet latitudinal fluctuations and the Summer North Atlantic OscillationThere is a robust positive relationship across models between the SNAO vorticity anomalies and the strength of the eddy‐mean flow feedbackA significant portion of the intermodel spread in precipitation persistence is linked to jet fluctuations persistence differences [ABSTRACT FROM AUTHOR]

In the mid‐latitudes extreme precipitation events are strongly associated with cold fronts. By exploring drivers across different scales and relating them to precipitation, we aim to improve our understanding of processes influencing cold frontal extremes. Using hourly ERA5 data over Europe and the North Atlantic, cold fronts are detected and the associated conditions are identified. Quantile regression models are employed to find drivers of frontal precipitation and to quantify these relations. Additionally, we use composites to study the synoptic conditions and meso‐scale structure of extreme events. We find that humidity close to the detected fronts, convergence and the low level jet speed contribute most to the formation of extreme precipitation. Synoptic conditions favoring the formation of extreme events are also identified. These results improve our understanding of cold frontal processes leading to precipitation. Additionally, they provide the foundation for a process‐based evaluation of frontal dynamics in climate models. Plain Language Summary: Extreme rain, snow and hail events in the mid‐latitudes are often caused by atmospheric cold fronts. To understand what makes certain events extreme, we need to study the processes and conditions influencing these fronts. In this study, we analyze the characteristics of cold fronts and their connection to extreme rain. First, we identify fronts in the ERA5 climate reanalysis data by finding strong changes in temperature and humidity over Europe and the North Atlantic. Next, we examine the conditions near these fronts. Using statistical models, we link these conditions to rain intensities. Additionally, the large‐scale weather patterns are studied by looking at the average conditions. We find that atmospheric moisture content and the strength of certain frontal circulation patterns play the biggest role in creating extreme weather. These results help us to better understand cold fronts and provide the basis for evaluating frontal conditions in climate models. Key Points: Cold fronts are responsible for up to 70% of hourly extreme precipitation over EuropeERA5 captures the key structural and circulation features of cold fronts that are expected from theoryMoisture content close to the front, low‐level jet speed and convergence are identified as the main drivers of frontal precipitation [ABSTRACT FROM AUTHOR]

Atmospheric blocking is characterized by persistent anticyclones that "block" the midlatitude jet stream, causing temperature and precipitation extremes. The traffic jam theory posits that blocking events occur when the Local Wave Activity flux, a measure of storm activity, exceeds the carrying capacity of the jet stream, leading to a pile up. The theory's efficacy for prediction is tested with atmospheric reanalysis by defining "exceedance events", the time and location where wave activity first exceeds flow capacity. The theory captures the Northern Hemisphere winter blocking climatology, with strong spatial correlation between exceedance and blocking events. Both events are favored not only by low carrying capacity (narrow roads), but also a downstream reduction in capacity (lane closures causing a bottleneck). The theory fails, however, to accurately predict blocking events in time. Exceedance events are not a useful predictor of an imminent block, suggesting that confounding factors explain their shared climatological structure. Plain Language Summary: An atmospheric block is a large, high pressure weather pattern that blocks the jet stream, affecting many regions in the midlatitudes including North America and Europe. Blocks are notable for their persistence, driving extreme weather conditions for up to a week or longer. Despite their significant societal impact, we don't fully understand the mechanism(s) that generate blocks. A traffic jam theory was proposed, which suggested that the onset of a block is caused by having too much "storm activity flux", which leads to a pile up of storm activity, just as a traffic jam is precipitated by conditions where the vehicular flux exceeding the road capacity, blocking traffic. This analogy is useful for understanding the preferred locations of atmospheric blocks in the time‐mean sense, but is not predictive in terms of individual blocking events. We further propose to incorporate additional regional constraints on flux capacity, analogous to "traffic bottlenecks", to improve our understanding of preferred blocking locations. Key Points: Flow capacity exceedance events, predictors of blocking onset in the traffic jam theory, are defined and evaluated in climate reanalysesA downstream reduction in flow capacity is ubiquitous for both exceedance and blocking events: lane closures favor traffic jamsBlocks are co‐located with exceedance events in space but not in time, limiting the utility of the traffic jam theory for prediction [ABSTRACT FROM AUTHOR]

In December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation. Plain Language Summary: In December 2022, North America was hit by an exceptionally severe cold event. Scientists have been trying to understand the reasons behind this cold wave. Using detailed weather data and climate models, researchers found that natural climate variability played a major role, causing temperatures to drop by about 5.14 degrees Celsius. On the other hand, external factors like human‐induced climate change had a minor effect, slightly reducing the severity of the cold by 0.42 degrees Celsius. The study identified an atmospheric wave train pattern, originating from the North Pacific and moving toward North America, which played a crucial role in triggering this extreme weather. Further investigations showed that this wave train was influenced by specific large‐scale weather patterns in the Pacific region, namely the Pacific‐North American (PNA) and North Pacific Oscillation (NPO) patterns, which contributed to the cold temperatures by approximately 2.18 and 2.12 degrees Celsius, respectively. Additionally, colder‐than‐normal sea surface temperatures in the tropical central Pacific, associated with La Niña, strengthened the wave train. Internal thermodynamical processes, such as increased snow cover, also slightly intensified the cold wave by reflecting more sunlight and reducing the amount of solar energy reaching the surface. Key Points: Internal variability triggers 2022 December extreme cold wave in North AmericaPNA and NPO are two key teleconnections responsible for this extreme cold waveSnow cover‐SAT feedback intensifies this extreme cold wave [ABSTRACT FROM AUTHOR]

The interannual variation of the South China Sea (SCS) summer monsoon onset (SMO) may bring extreme weather and climate disasters in East Asia. However, its skillful forecast still remains challenging. This study investigates the intraseasonal ocean‐atmosphere interaction that affects the SCSSMO through diagnostic analysis and numerical experiments. It reveals that the cold sea surface temperature in the Southern SCS during winter (referred as cold tongue, CT) is the key pathway controlling the propagation of the 30–60 days intraseasonal oscillation (ISO) convective system from the Bay of Bengal (BOB) to the SCS. The CT variations affect the interannual variation of the SCSSMO. Specifically, the strong (weak) CT after the peak of La Niña (El Niño) years suppresses (enhances) the propagating ISO from the BOB to the SCS, resulting in a delayed (advanced) SCSSMO. This finding offers the new scientific insights for improving the forecasting of the SCSSMO. Plain Language Summary: The occurrence of a strong cold tongue (CT) in winter, commonly detected in the southern South China Sea (SCS) during La Niña decay years, plays a significant role in delaying the SCS summer monsoon onset (SCSSMO). It adversely affects the northeastward progression of the 30‐60‐day convective Intraseasonal Oscillation (ISO) from the equatorial Indian Ocean to the southern SCS. This interference leads to the delayed onset of the SCSSMO. It highlights the crucial influence of the CT sea surface temperatures (SST) on the linkage between Bay of Bengal and SCS at the intraseasonal scale, thus significantly impacting the timing of the SCSSMO. Key Points: The winter cold tongue in South China Sea (SCS) is a crucial oceanic precursor for the interannual variation of the SCS summer monsoon onset (SCSSMO)Strong cold tongue in the SCS can suppress the propagation of the 30‐60‐day intraseasonal oscillation into the SCSStrong SCS cold tongue during La Niña years tends to delay the SCSSMO [ABSTRACT FROM AUTHOR]

We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40‐year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6‐hourly TC snapshots in the control and find a corresponding "twin" in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture‐related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short‐term intensity variability. Plain Language Summary: We examined how tropical cyclones (TCs) near the United States might change due to warmer climates. First, we recreated past weather conditions from 1980 to 2019 using a specialized climate model. Then, we predicted future changes in the same meteorology by modifying the model to include warmer temperatures and running four additional simulations. We tracked and analyzed over 4,000 instances of TCs from the past 40 years, ensuring that these TCs were all matched between all five simulations, and evaluated their changes under future warming conditions. Our findings suggest that TCs will become stronger on average as the world warms, with the most notable increases in TC moisture. Not every TC in our study becomes more intense with warming, however, highlighting the complexity of understanding how extreme weather will change in the future. We found that the chances of a TC rapidly intensifying or suddenly weakening both rise with higher levels of warming. These trends could complicate predictions of TC intensity in the future, presenting additional challenges for forecasting and preparation. Key Points: Thermodynamic global warming simulations provide a unique large‐sample size view into paired counterfactual tropical cyclones12 km simulations indicate future near‐United States cyclones will, on average, be wetter, slightly more intense, but similarly sizedPeriods of rapid intensification and weakening indicate increased tropical cyclone intensity variability in warmer climates [ABSTRACT FROM AUTHOR]

Dust and high cloud interactions are critical for climate change, primarily due to the dominant roles of high cloud in the greenhouse effect and the continental precipitation. Nonetheless, disentangling the specific impacts of dust from the overlying meteorology influence on high clouds presents great challenges. In this study, we construct a meteorological pattern that successfully reveal the intricate connection between high cloud distribution and atmospheric conditions. Through this strong bounded relationship, we find that dust exhibits notable controls over the facilitation or inhibition of ice particle growth contingent upon the prevailing meteorological fields. More dust can increase precipitation rate and shorten high cloud lifetime particularly under favorable meteorology. These findings underscore the crucial role of dust concentration in mitigating global warming for future climate change. Plain Language Summary: Dust particles in the atmosphere play a crucial role in the formation and dissipation of high clouds, which can significantly impact global climate and weather patterns. By analyzing satellite observations and climate model data, we discovered that dust can either enhance or suppress the growth of ice crystals, depending on the atmospheric conditions. In moist environments with favorable weather, increased dust leads to larger ice crystals and more precipitation, accelerating the life cycle of high clouds. Conversely, in dry conditions with unfavorable weather, excessive dust competes for limited moisture, resulting in smaller ice crystals, although it can still increase rainfall during the dissipation stage of high clouds. As climate change progresses and dust storms become more frequent, the increased presence of dust in the atmosphere may lead to more extreme weather events and potentially mitigate some of the warming effects caused by high clouds. Understanding these complex interactions between dust, high clouds, and weather conditions is essential for improving climate models and predicting future changes in Earth's water cycle and energy balance. Key Points: Extracted first principal component, highly correlated with high cloud fraction, isolating meteorological effects on cloudsDust facilitates ice crystal formation and precipitation, catalyzing cloud lifecycle and ultimately modifying radiative effectsIncreased dust accelerates high cloud lifecycle, causing more extreme weather and mitigating climate warming [ABSTRACT FROM AUTHOR]

Compound warm extremes exert profound impacts on environment, health, and socioeconomics. Yang et al. (2024) indicated a shift or transition from warm‐dry extremes (WDEs), common in non‐ice‐covered areas, to warm‐wet extremes (WWEs) in ice‐covered zones. Utilizing ERA5 reanalysis data, this study determined the duration and frequency of WDEs and WWEs across ice‐covered and non‐ice‐covered regions. A comprehensive analysis uncovers the physical mechanisms responsible for the paradigm differences and attributes them to the weakening of land‐atmosphere interaction caused by ice‐cover, which inhibits soil moisture feedback and reduces the intensity and duration of warm events in ice‐covered areas. Both WDEs and WWEs are associated with high‐pressure systems (HPs). WDEs, situated directly beneath HPs, intensify due to adiabatic warming from subsidence motions. Conversely, WWEs, located beneath the poleward fringes of HPs, emerge from advective warming and moistening associated with poleward intrusions of warm‐moist air. Plain Language Summary: Significantly rising temperatures can coincide with extreme weather events such as droughts or rainstorms to form compound warm‐dry extremes (WDEs) or warm‐wet extremes (WWEs), leading to more severe consequences than just hot weather alone. Recent findings indicate that WDEs are more prevalent in non‐ice‐covered regions where people reside and crops are cultivated, while WWEs are more common in ice‐covered areas. This study seeks to comprehend the reasons behind the paradigm differences of extreme events by analyzing the conditions during hot summers. Our research has identified two primary causes for the paradigm differences. First, in non‐ice‐covered regions, the ground warms the overlying air, which in turn dries out the soil, leading to even higher temperatures. This explains why more WDEs are observed in these areas. However, in ice‐covered regions, this process is inhibited by ice. Second, the high‐pressure systems accompanying these extreme events behave differently based on the presence or absence of ice. In non‐ice‐covered regions, these systems are located directly above the regions, exacerbating the hot‐dry conditions by pushing air downwards. Conversely, hot‐wet conditions occur in ice‐covered regions because these areas are situated beneath the poleward fringes of the systems, which transport warm and moist air from the lower latitudes. Key Points: In contrast to midlatitude regions where warm‐dry extremes are common, warm‐wet extremes prevail over polar regionsPoleward circulation associated with high pressure systems, rather than subsidence, produces warm‐wet conditionsSnow‐ and ice‐covered surfaces neutralize the land‐air interaction feedback loop that reinforces hot‐dry spells in midlatitude regions [ABSTRACT FROM AUTHOR]

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression‐based Bayesian Optimisation (G‐BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. Plain Language Summary: This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression‐based Bayesian Optimisation (G‐BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G‐BO, we identified more effective values that substantially improved the model's simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions. Key Points: Our study optimizes WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulationG‐BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidityResults suggest updating WRF model's default settings for better extreme heat event simulations [ABSTRACT FROM AUTHOR]

The mid‐latitude extreme weather disasters are often associated with explosively developing cyclones (ECs). Based on different vertical development characteristics, 4,608 ECs identified over the North Pacific in the cold season of 44 years of NCEP‐CFSR reanalyzes are divided into four types of upward development and four types of downward development categories. ECs with vertical upward (downward) development follow a northeastward (nearly eastward) path, mainly explosively developing over the Northwest Pacific (Asia continent and Pacific). Furthermore, utilizing the piecewise potential vorticity inversion method reveals the synergetic forcing of the turbulent heat transport and baroclinicity in the lower troposphere, the latent heat release in the middle levels, the upper‐level jet stream, and the downward intrusion of stratospheric potential vorticity on the ECs. Different configurations of these influences from the troposphere to the stratosphere result in the occurrences of eight types of ECs in the cold season over the North Pacific. Plain Language Summary: Since the late twentieth century, there has been a significant variation in the frequency of explosively developing cyclones (ECs), which bring extreme weather disasters to the mid‐latitudes. This study classifies winter North Pacific ECs into four upward and four downward developing categories. The upward developing ECs include those enhanced only in the lower troposphere (UL‐EC), those developing upward to the middle troposphere (UM‐EC), those developing upward to the upper troposphere (UU‐EC), and those developing upward into the stratosphere (US‐EC). In contrast, the downward developing ECs can be classified as those intensifying only in the upper troposphere (DU‐EC), those developing downward to the middle troposphere (DM‐EC), those developing downward to the lower troposphere (DL‐EC), and those developing downward from the stratosphere (DS‐EC) to the lower troposphere. Furthermore, the piecewise potential vorticity inversion results show that the explosive development of UL‐EC and UM‐EC are dominated by the thermodynamical processes in the middle‐to‐lower troposphere, exhibiting a baroclinic structure with opposite anomalous geopotential height between the troposphere and stratosphere. However, the other types have a consistent stratospheric‐to‐tropospheric negative anomalies, which is determined by the upper‐layer dynamical processes. The downward intrusion of stratospheric potential vorticity dominates the explosive development for the US‐EC and DS‐EC. Key Points: The North Pacific explosively developing cyclones during cold season are divided into eight up‐ and down‐ward vertically developing typesThe explosively developing cyclones are proved to be an important link between the stratosphere and troposphere in the mid‐high latitudesUsing the piecewise potential vorticity inversion method, the quantitative forcings on the explosively developing cyclones are explored [ABSTRACT FROM AUTHOR]

Large‐scale topographies affect global extreme weather and climate though dynamic and thermal forcing. However, the impacts of typical topographies on heat waves in different drylands globally remain unclear. In this study, we find that heat waves are mainly occurred in global drylands during 1940–2022. The frequencies and intensities of heat waves in global drylands have significantly increased after 1980s. Multiple numerical model simulations reveal that the impact range of Asian topography on heat waves in global drylands is widest, not only in East Asia, Central, and west Asia locally, but also can reach as far as North Africa and North America. While the impact ranges of topographies in Africa, Arabian Peninsula, North America, and South America on heat waves in drylands are relatively narrow, which are concentrated in localized and their surroundings. These conclusions could provide clues to understand the influence of topography on global weather and climate extremes. Plain Language Summary: Topographies have important impacts on heat waves in global drylands. Through numerical model simulations, this study reveals that Asian topography contributes significantly to heat waves in global drylands, with the widest impact ranges which can cover drylands of East Asia, Central and west Asia, North Africa, even North America. The impacts of the East African Plateau, the Arabian Plateau, the Sierra Madre in North America and the Andes in South America on heat waves are mainly in the local and surrounding regions. Key Points: Heat waves are mainly occurred in global drylands and experience significant increases after 1980sThe impact range of Asian topography on heat waves in global drylands is widest, which can reach as far as North Africa and North AmericaThe topographies in Africa, Arabian Peninsula, North America, and South America can only affect heat waves in localized and surroundings [ABSTRACT FROM AUTHOR]

Climate change is creating a new era of infectious disease crises, further exacerbated by extreme weather. However, the relationship between extreme weather and infectious disease remain unclear. Here, we provide a new quantitative study on the impact of cold wave on COVID‐19 as an example. We found that during cold waves, extreme cold temperatures coupled with rapid aerosol transport accelerated COVID‐19 outbreaks. It directly increased the number of COVID‐19 cases in Beijing by 28.1% in the winter of year 2022. More urgently, cold temperatures led to a higher risk of death during infectious disease outbreaks, with a 7.07% increase in confirmed deaths and a 16.61% increase in excess mortality. Our findings emphasize the urgent need to promote a synergistic policy for responding to infectious diseases during cold wave disasters in order to minimize the risk of death among the elderly and those with underlying diseases. Plain Language Summary: The sudden drop in temperature and rapid spread of aerosols during cold waves resulted in a higher prevalence of infectious diseases. At the same time, low temperatures resulted in a higher risk of death due to infectious diseases and a variety of other factors during the epidemic. Key Points: The rapid dispersion of aerosols during cold waves hastens the transmission of infectious diseasesLow temperatures contribute to higher risk of death during infectious disease outbreaks [ABSTRACT FROM AUTHOR]

A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project. Plain Language Summary: The projected loss of Arctic sea‐ice under 2°C global warming will cause large warming in the Arctic region and climate and weather anomalies outside the Arctic. The warming in the Arctic will mean warmer airmasses coming from the Arctic and also more moisture from the open Arctic Ocean. Furthermore, it will also change atmospheric circulation. These effects together will determine the impacts of Arctic warming. In this study, we introduce a novel sub‐sampling method to isolate atmospheric circulation change in response to the Arctic warming. The method involves selecting members of simulations from the experiment with future Arctic sea‐ice conditions, the average of which is equal to the average of the members of simulations in the experiment with present‐day Arctic sea‐ice conditions. We found that atmospheric circulation change in European regions is relatively weak so that warming effects will dominate the climate and weather response there. On the other hand, atmospheric circulation change will dominate the climate and weather response in East Eurasia. We also found that stronger atmospheric circulation changes will generally increase the response to the Arctic warming. We suggest caution when assessing whether different responses in different models can be interpreted as true differences in model physics. Key Points: A novel sub‐sampling method is introduced to isolate the role of dynamics in the response to projected Arctic sea‐ice lossA dynamical Siberian High response dominates the temperature response over East Eurasia while that of the North Atlantic Oscillation is weakInter‐model differences in Polar Amplification Model Intercomparison Project likely contain a large fraction of internal variability due to the unconstrained dynamic effects [ABSTRACT FROM AUTHOR]

Contrasting the extensive research on summer atmospheric rivers (ARs) in the Antarctic Peninsula (AP), winter AR impacts are less understood. This study examines a unique warming event from 1 to 3 July 2023, using in situ winter observations and ERA5 reanalysis. On 2 July, Frei station experienced an extreme warm event with a temperature of 2.7°C and a significant rise in the freezing level, coinciding with winter rainfall. A pressure dipole pattern over the AP, with contrasting circulations over Bellingshausen and Weddell Seas, facilitated an AR, carrying warm, humid air initially from South America/Atlantic and then the southeast Pacific. This shift resulted in anomalous water stable isotope composition in precipitation. Trends suggest a strengthening winter pressure dipole, associated with increased AR frequency and higher temperatures in northern AP. These findings highlight the importance of winter observations in exploring AR impacts, bridging knowledge gaps about winter AR behaviors. Plain Language Summary: The Antarctic Peninsula is increasingly witnessing climate extremes during summer, while the understanding of such extremes in winter remains limited. Our study explores a significant warming event in the northern Peninsula in early July 2023, utilizing recent winter in situ observations and atmospheric analysis. On 2 July, an exceptional temperature of 2.7°C was recorded, significantly altering the atmospheric freezing level and causing rainfall instead of snow. Our analysis revealed a unique atmospheric pattern around the Peninsula, characterized by varying air movements over the Bellingshausen and Weddell Seas. This pattern facilitated an "atmospheric river," a flow of warm and moist air from lower latitudes, including continental South America/Atlantic and the southeast Pacific, as reflected in the precipitation's water stable isotope composition. This event is indicative of a potential trend toward more frequent and severe occurrences, emphasizing the urgent need for comprehensive winter research in the Antarctic Peninsula to understand the broader implications of extreme weather events and climate change. Key Points: Early July 2023 had extreme winter warmth with rainfall in the northern Antarctic Peninsula, favored by an atmospheric riverIn recent decades, strengthened winter pressure dipole has directed northerly warm and moist air, resulting in warm events in the regionWe highlight the need for expanded winter research in the Antarctic Peninsula to better understand climate change and extreme weather events [ABSTRACT FROM AUTHOR]

Despite the significant socioeconomic implications in the link between atmospheric waviness and extreme weather events, future atmospheric waviness trends remain elusive due to uncertainties arising from diverse definitions and insufficient dynamical formalism in existing metrics. This study employs a local wave activity (LWA) metric, whose prognostic equation links wave activity changes to forcing mechanisms, to assess wintertime Northern Hemisphere waviness in ERA5 and HighResMIP data sets. The models generally exhibit high fidelity in reproducing observed waviness, while biases stem primarily from biases in the LWA source, low‐level meridional heat flux, which tend to improve with higher resolutions. Future projections exhibit reduction in LWA, primarily due to suppressed LWA generation, which is mitigated by higher‐resolution models. We found that both biases and reduction of the LWA source are closely associated with sensible heat fluxes from the ocean to the atmosphere, highlighting the potential impacts of resolving ocean currents. Plain Language Summary: This study investigates atmospheric waviness in the Northern Hemisphere during winter, using a measure called local wave activity, which enables identifying causes behind waviness changes. Applying this metric to various climate models with different resolutions and a reanalysis data set, we found that historical simulations by the models generally well capture the observed climatological waviness. Biases are found over Europe and mid‐latitudes in Eurasia and the Pacific, which generally improve with higher‐resolution models. In the future simulation, reduction in waviness over much of the Northern Hemisphere, especially in the Atlantic and Pacific sectors, due to a decreased waviness source is found. Higher‐resolution models mitigate this reduction over North Atlantic. Our findings emphasize the connection between waviness and heat given from the ocean to the atmosphere, highlighting the importance of higher ocean resolution for accurate future waviness projections. This study underscores the significant role of the ocean in shaping atmospheric waviness and its implications for climate predictions. Key Points: Biases in Northern Hemisphere wintertime climatological waviness in historical runs are mostly ameliorated with high‐resolution modelsFuture Northern Hemisphere winter waviness will undergo a general reduction, which is mitigated by high‐resolution models over North AtlanticSensible heat fluxes play a crucial role in both historical biases and future reductions in waviness, highlighting the key role of the ocean [ABSTRACT FROM AUTHOR]

Mid‐latitude extreme cold outbreaks are associated with disruptions of the polar vortex, which often happen abruptly in connection to a sudden stratospheric warming. Understanding global warming (particularly Arctic amplification) impacts on forecasting such events is challenging for the scientific community. Here we apply clustering analysis on the Northern Annular Mode to identify surface precursors and the governing mechanisms causing polar vortex disruption events. Two clusters of vortex breakdown emerge; 65% of the events, mainly displacements, are associated with high‐latitude Ocean warming in the North Pacific and in Barents‐Kara Sea. Such warming may cause large scale modifications of the tropospheric flow that favors a slowdown of the stratospheric vortex. The persistence of Ocean surface temperature patterns favors polar vortex disruptions, potentially improving prediction skills at the sub‐seasonal to seasonal time scales. Plain Language Summary: Extreme winter weather is linked to cold arctic outbreaks when the polar air mass spills frigid air to mid‐latitudes. This phenomenon often follows weak polar vortex (in extreme cases a Sudden Stratospheric Warming) episodes. Forecasting such events is challenging as many climatic components can be involved. In this study, it is found that 65% of the events start with a certain surface air mass distribution (Low Pressure over the North Pacific, High Pressure over Eurasia). Such distribution is triggered by high latitude ocean warming corresponding to warm temperature anomalies in the North Pacific Ocean and Sea Ice loss in Barents‐Kara seas. This result helps predicting the probability of polar vortex disruptions in winter, potentially leading to enhanced sub‐seasonal to seasonal cold outbreaks forecast. Key Points: Around 65% of weak polar vortex (WPV) events are preceded by tropospheric pressure anomaliesHigh‐latitude ocean warming explains tropospheric air mass modification, which favors upward wave flux that disrupts the stratosphereProbabilistic forecast of WPV events is possible using an index of high‐latitude ocean warming [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

Abstract Climate change is creating a new era of infectious disease crises, further exacerbated by extreme weather. However, the relationship between extreme weather and infectious disease remain unclear. Here, we provide a new quantitative study on the impact of cold wave on COVID‐19 as an example. We found that during cold waves, extreme cold temperatures coupled with rapid aerosol transport accelerated COVID‐19 outbreaks. It directly increased the number of COVID‐19 cases in Beijing by 28.1% in the winter of year 2022. More urgently, cold temperatures led to a higher risk of death during infectious disease outbreaks, with a 7.07% increase in confirmed deaths and a 16.61% increase in excess mortality. Our findings emphasize the urgent need to promote a synergistic policy for responding to infectious diseases during cold wave disasters in order to minimize the risk of death among the elderly and those with underlying diseases.

Clustering extreme weather events are concurrent or consecutive occurrences of disastrous weather in multiple regions, resulting in cumulative impacts. Here we discovered a significant increasing trend in clustering extreme precipitation events over the Indo‐Pacific rim over the past four decades. This trend can be largely attributable to the increasing frequency of the Rossby wave response, including the circum‐Pacific and cross‐Pacific patterns due to Rossby wave activity propagation, and the Pacific anticyclone pattern due to Rossby wave breaking. The three patterns show remarkable disparity in seasonality, persistence, and hydrological impacts. They can increase the occurrences of most severe precipitation by up to 5, 8, and 25 times, respectively. The Indian Summer Monsoon heat sources and La Niña are identified as key drivers, and the mid‐latitude jet streams are modulators contributing to the events. Our findings suggest that specific Rossby wave patterns may influence the potential evolution of future clustering extremes. Plain Language Summary: Extreme precipitation events that occur concurrently or consecutively in multiple regions over a period, can have a cascading effect on human livelihoods. This is a new type of disastrous weather event in the context of climate change, which we call clustering extreme precipitation events. However, there is a lack of understanding about their frequency and underlying mechanisms. To address this, we propose an identification method for clustering extreme precipitation events over the Indo‐Pacific Rim. Here, our findings reveal that three distinct Rossby wave patterns are responsible for these events. Accompanied by more frequent occurrence of the identified Rossby wave patterns, the frequency of clustering extreme precipitation events has increased (0.5 event/decade) in the past 42 years. The Rossby wave patterns set up favorable atmospheric conditions and significantly increase the occurrences of the most severe precipitation in the Northern Hemisphere. The Indian Summer Monsoon heat sources serve as a first‐order driving mechanism for exciting the Rossby waves, while La Niña serves as external forcing. Additionally, the variations of mid‐latitude jet streams modulate the development of the Rossby wave patterns. The results provide a predictability source for the sub‐seasonal to seasonal community. Key Points: Over the past 42 years, there has been an increase in the frequency of clustering extreme precipitation events across the Indo‐Pacific rimThe consecutive occurrences of extreme precipitation in multiple regions are organized by three diverse Rossby wave patternsThe Indian Summer Monsoon heat sources and La Niña are key drivers of the events and the mid‐latitude jet streams are modulators [ABSTRACT FROM AUTHOR]

The South Asian summer monsoon strongly modulates regional temperature and humidity. While extreme dry heat peaks in the pre‐monsoon season, recent literature suggests that extreme humid heat can continue to build throughout the monsoon season. Here we explore the influence of monsoon onset and subseasonal precipitation variability on the occurrence of extreme wet bulb temperatures (Tw) across South Asia. We find that extreme Tw events often occur on rainy days during the monsoon season. However, the influence of precipitation on Tw varies with the background climatology of surface specific humidity. In climatologically drier areas, positive Tw anomalies tend to occur when precipitation increases due to either early onset or wet spells during the monsoon. In contrast, in climatologically humid areas, positive Tw anomalies occur during periods of suppressed precipitation, including both delayed onset and dry spells during the monsoon. Plain Language Summary: The combination of high heat and humidity poses greater risks to human health, productivity, and well‐being relative to elevated temperatures alone. South Asia has already experienced some of the most extreme humid heat observed on Earth. Typically, the highest temperatures in South Asia occur during the pre‐monsoon season in March‐May. While the precipitation and cloudiness associated with the summer monsoon reduce extreme air temperatures, the increased humidity can contribute to the occurrence of extreme humid heat events. Our research finds that a majority of extreme Tw events across South Asia happen on rainy days during the monsoon season. This is especially true for regions where surface humidity is typically low during the monsoon season, and Tw tends to be higher during heavier than usual precipitation spells and during years when the monsoon season starts earlier in the calendar year. In regions with high background surface humidity, however, Tw is more intense when there is less precipitation than usual or when the monsoon onset is delayed. These results help to identify additional times with high risk of heat stress for outdoor workers, and provide a new way of understanding how monsoon variations influence local humid heat. Key Points: In contrast to dry heat, most extreme wet bulb temperature events in South Asia take place on rainy days during the monsoon seasonLocal background specific humidity determines whether wet bulb temperatures are intensified by enhanced or suppressed precipitationBaseline moisture availability also influences whether early or delayed monsoon onset intensifies wet bulb temperature anomalies [ABSTRACT FROM AUTHOR]

Headwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity‐estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effects of the shape of the weathered bedrock and the associated storage capacity on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the weathered bedrock affects not only the magnitude but also the peak time of both streamflow and subsurface dynamic storage. Plain Language Summary: In mountainous terrains, precipitation falling as snow and rain not only flows into streams but also infiltrates into the subsurface, replenishing the subsurface water storage. This stored water in the subsurface (including both soil moisture and rock moisture) supports various ecohydrological processes such as groundwater recharge and plant water use. In this study, we use computational simulation to study how the below‐ground heterogeneity affects the partitioning of precipitation into streamflow, subsurface water storage, plant water use, and deep groundwater recharge. Here, below‐ground heterogeneity refers to the depth extent of weathered rock at the hillslope‐ or catchment‐scale. Simulation results show that both the annual magnitude and weekly variations of the streamflow and subsurface storage change noticeably as the below‐ground weathering pattern varies, in particular under a snow‐dominated weather scenario. The outcome of this study will potentially help stakeholders develop more informed water management practices in the face of a warming climate and more extreme weather events. Key Points: Integrated hydrologic modeling of catchments with different subsurface critical zone structures was performedSimulation results show that the shape of the weathered bedrock and associated storage capacity affect various hydrologic processes in the catchmentThe effect of weathered bedrock on hydrological partitioning is more significant under weather conditions featuring large snow fractions [ABSTRACT FROM AUTHOR]

A severe hailstorm that occurred in Spain on 30 August 2022, caused material and human damage, including one fatality due to giant hailstones up to 12 cm in diameter. By applying a pseudo‐global warming approach, here we evaluate how a simultaneous marine heatwave (and anthropogenic climate change) affected a unique environment conductive to such giant hailstones. The main results show that the supercell development was influenced by an unprecedented amount of convective available energy, with significant contributions from thermodynamic factors. Numerical simulations where the marine heatwave is not present show a notable reduction in the hail‐favorable environments, related mainly to modifications in thermodynamic environment. Our simulations also indicate that the environment in a preindustrial‐like climate would be less favorable for convective hazards and thus the hailstorm event would likely not have been as severe as the observed one, being possible to perform a novel attribution of such kind. Plain Language Summary: In August 2022, northeastern Spain faced a damaging hailstorm with hailstones up to 12 cm, causing significant harm and one fatality. This study examined how hail‐favorable environments were modified by a marine heatwave and human‐induced climate change. Numerical simulations show that the storm's intensity was influenced by abundant atmospheric energy and moisture from the warm sea, partly influenced by human‐induced warming. When the warm sea factor was excluded from the simulations, hailstones were smaller. These findings emphasize the role of human‐induced climate change and warm sea surface temperature events in intensifying extreme and high‐impact weather events like hailstorms. Key Points: Unprecedented hailstones in Spain (up to 12 cm) caused widespread damage and one fatalityA record‐breaking marine heatwave enhanced the strong hail‐favorable environmentNovel demonstration of an extreme hailstorm event attribution to anthropogenic warming [ABSTRACT FROM AUTHOR]

Subseasonal reversal of warm Arctic–cold Eurasia (SR‐WACE) pattern has significant impacts on transitions of weather and climate extremes in Eurasia. This study explored the performances of climate models to simulate the main features of SR‐WACE. For real‐time predictions, most of the state‐of‐the‐art climate models showed limited ability to accurately forecast SR‐WACE in advance. Furthermore, most of the historical simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) had also difficulties in well simulating the SR‐WACE. Further exploration showed that the simultaneous reversal of the Ural blocking high (UB) and Siberian high (SH) is the key atmospheric driver of the SR‐WACE occurrences, which were verified by both of the real‐time predictions and historical simulations. Our results implied that the simulation of SR‐WACE was a huge challenge and the critical solutions included improving simulation of subseasonal reversals of UB and SH in the atmosphere. Plain Language Summary: We explored the performances of climate models to simulate the main features of subseasonal reversal of warm Arctic–cold Eurasia (SR‐WACE) pattern. For real‐time predictions, most of the state‐of‐the‐art climate models showed limited ability to accurately forecast SR‐WACE in advance, which possibly restricted the subseasonal to seasonal forecasts skill of climate anomalies in mid‐low latitudes. Furthermore, the historical simulations from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) had also difficulties in well simulating the SR‐WACE. The most of CMIP6 models only reproduce the SR‐WACE phenomenon with higher order modes and lower explained variances than observations. We further explored the atmospheric circulation associated with the SR‐WACE in CMIP6 models and found that when a strong reversal of the Ural blocking high and Siberian high (SH) is simultaneously simulated can a SR‐WACE occur. This result was also validated in real‐time prediction models. Therefore, the simulation of SR‐WACE was a huge challenge and the critical solutions included improving simulation of subseasonal reversals of UB and SH in the atmosphere. Key Points: Most of climate models had difficulties in well simulating the subseasonal reversal of warm Arctic–cold Eurasia (SR‐WACE) patternMost of CMIP6 models only reproduce the SR‐WACE phenomenon with higher order modes and lower explained variances than observationsThe simultaneous reversal of the Ural blocking high and Siberian high is key for simulating the SR‐WACE [ABSTRACT FROM AUTHOR]

We investigate the response of surface temperature persistence, quantified using a lagged autocorrelation, to imposed Arctic sea‐ice loss in coupled model experiments. Sea‐ice loss causes increases in persistence over ocean in midlatitudes and the low‐Arctic, which are of a similar magnitude to the total response to climate change in these regions. Using an idealized model, we show that sea‐ice loss induces a slowing of meridional wind anomalies, which can drive the midlatitude persistence increase obtained in coupled models. Sea‐ice loss should induce persistence increases in the Arctic, through its effect on the surface heat capacity. However, in coupled models with imposed sea‐ice loss, persistence increase in the Arctic is essentially absent. We suggest that methods used to constrain sea‐ice in coupled models may spuriously reduce the effects of sea‐ice loss on persistence. Plain Language Summary: It has been suggested that Arctic sea‐ice loss is driving an increase in extreme weather events in Northern Hemisphere midlatitudes. We discuss two routes through which Arctic sea‐ice loss can increase the persistence of weather in the Arctic and midlatitudes. First, sea‐ice loss increases the thermal inertia of the surface by exposing open ocean, which has a higher heat capacity, potentially leading to persistence increases in the Arctic. Second, sea‐ice loss drives changes in atmospheric circulation, which may affect surface temperature variability. We analyze climate model experiments where sea‐ice loss is artificially induced, in the absence of other climate forcings, to investigate whether either of these routes leads to an appreciable change in the persistence of surface weather. We find sea‐ice loss causes modest increases in persistence in midlatitudes, with the most significant changes occurring over ocean regions. Unexpectedly, we do not identify an increase in Arctic persistence in these experiments, even though this response is easily identifiable in experiments driven by greenhouse gas emissions. Using a simplified climate model, we show that underestimating the persistence response may be an unintended side‐effect of the methods used to isolate the effect of sea‐ice loss in climate models. Key Points: Arctic sea‐ice loss drives local increases in persistence by reducing the effective heat capacity of the surfacePersistence in midlatitudes increases due to changes in the forcing of surface temperature variability by atmospheric circulationThe effect of sea‐ice loss on persistence may be underestimated in comprehensive models with constrained sea‐ice [ABSTRACT FROM AUTHOR]

The mid‐high latitude intraseasonal oscillation (ISO) has remarkable impacts on the Northern Hemisphere. However, the interannual variance of the mid‐high latitude ISO and its underlying mechanism are rarely explored. Here, we find that the tropical stratospheric Quasi‐Biennial Oscillation (QBO) has notable impacts. A statistically significant contrast in the interannual variability of intraseasonal 2‐m temperature (T2m) over northern Eurasia is detected between the easterly (EQBO) and westerly (WQBO) QBO phase. The influence of the QBO could be attributed to the interactions of ISO perturbation and QBO‐related winter‐mean flow. Specifically, the ISO perturbation is more efficient in extracting kinetic and potential energy from the winter‐mean flow in EQBO. Additionally, the northern Eurasia ISO propagates southeastward and exert effects on China. Due to this, the subseasonal forecast system of NUIST (NUIST CFS1.1) displays higher skill in predicting the T2m in China during EQBO phase at 3, 4, and 5 pentads in advance. Plain Language Summary: Extreme weather and climate in winter, such as cold waves, snowstorms, and prolonged low temperature, have severe impacts on the society, therefore it is of great value to improve subseasonal prediction of 2‐m temperature (T2m). In this study, we find that the variability of intraseasonal T2m over northern Eurasia varies from year to year during boreal winter, which is negatively correlated with the tropical stratospheric Quasi‐Biennial Oscillation (QBO). The variability of intraseasonal T2m is enhanced during QBO easterly phase (EQBO). In accordance with the higher variance of intraseasonal T2m, the extreme temperature events display higher frequency and stronger intensity in EQBO. We also find that the intraseasonal oscillation (ISO) in the northern Eurasia is not stationary, it often propagates southeastward, impacting the weather and climate in China. This has implications for the prediction of intraseasonal T2m in China. These results help understanding the relationship between the tropical QBO and mid‐high latitude ISO and provide a potential way to improve the subseasonal forecasts. Key Points: The interannual variability of intraseasonal 2‐m temperature (T2m) is amplified during easterly Quasi‐Biennial Oscillation (EQBO) in winterThe likelihood of extreme temperature events, particularly cold extreme events with intensified high‐pressure systems, is enhanced in EQBOThe southeastward propagation of the intraseasonal oscillation over northern Eurasia has impacts on winter temperature in China [ABSTRACT FROM AUTHOR]

Global warming is expected to have far‐reaching impacts on the frequency and intensity of extreme events, but the effects of anthropogenic warming on the emergence seasons of year‐maximum near‐surface wind speed (NSWS) remain poorly understood. We provide a comprehensive map of the changing emergence seasons of year‐maximum NSWS using Coupled Model Intercomparison Project Phase 6 projections. Our analysis reveals a rapid response of synoptic‐scale extreme NSWS to global warming, with consistent spatial patterns observed across various periods and warming scenarios. The most significant increase (∼16%) in the emergence season is projected to occur in December‐January‐February (DJF) over Mid‐high‐latitude Asia by the end of the 21st century. The study also anticipates changes in the emergence seasons of year‐maximum NSWS at a regional scale. These results deepen our understanding of the complex and interconnected nature of global climate change and underscore the need for concerted efforts in addressing this pressing challenge. Plain Language Summary: Global warming is indisputably triggering changes in the world's weather systems, leading to more frequent and intense extreme weather events. However, it is unclear how anthropogenic warming affects the timing of the annual strongest near‐surface wind speed (NSWS). In this study, we used state‐of‐the‐art global climate models to create a comprehensive map illustrating these NSWS patterns of response to global warming. We discovered that these changes are consistent across various time periods (near to long term) and warming scenarios (low to high warming), revealing a robust relationship between extreme NSWS and global warming. The most significant change is observed during December‐January‐February in Mid‐high‐latitude Asia, with an increase of about 16% by the end of the 21st century. Our findings suggest that we can expect more year‐maximum NSWS occurs in different regions during specific seasons: December‐January‐February in North America and Asia, March‐April‐May in Africa, June‐July‐August in Asia and West Africa, and September‐October‐November in South America and Australia. These results offer valuable insights for guiding adaptation efforts even if ambitious climate actions manage to limit global warming at a lower level. Key Points: Changing emergence seasons of the land year‐maximum near‐surface wind speed (NSWS) map is createdThere is a rapid response of emergence seasons of year‐maximum NSWS to anthropogenic warmingThe strongest increase (16%) in emergence season is projected to occur in December‐January‐February over Mid‐high‐latitude Asia [ABSTRACT FROM AUTHOR]

Weather regimes have been defined over multiple regions and used in a range of practical applications, including subseasonal‐to‐seasonal forecasting and climate model evaluation. Despite their widespread use, the extent to which regimes reflect physical modes of the atmosphere is seldom investigated. Here, we adopt a year‐round classification of four North American weather regimes, with a fifth "no regime" class, and leverage dynamical systems theory to investigate their dynamical properties. We find that when the atmospheric flow is assigned to a regime, it displays persistent characteristics and a lifecycle‐like temporal evolution. We further find that, regardless of season, these characteristics are enhanced when the atmospheric flow displays a comparatively strong projection onto the cluster‐mean of the regime to which it is assigned (while the reverse is true for a weaker projection). We interpret these results as evidence that the four North American weather regimes are physically‐meaningful, with a clear dynamical footprint. Plain Language Summary: Surface weather and extremes in North America can be related to a small number of atmospheric patterns stretching over scales of thousands of kilometers. These patterns, known as weather regimes, vary over timescales of several days to weeks and occur repeatedly: depending on the exact definition, between a third and a fifth of all days typically fall within a given regime. Weather regimes have a number of practical applications, hinging around making statistical predictions of the weather over a large region several weeks in advance. Even though they have proven useful in practice, there is no simple way of determining to what extent weather regimes are actually linked to the dynamics and physics of the atmosphere. While different definitions of weather regimes exist for different seasons, there have been recent efforts to identify weather regimes that may be applicable year‐round. In this study, we adopt a four‐regime, year‐round classification for the North American continent, and investigate whether the regimes have a clear physical footprint. We find that when the atmosphere falls within a given regime, it displays persistent flow characteristics with a systematic evolution in time. These results support interpreting the regimes as physically‐meaningful, rather than simply a statistical characterization of atmospheric patterns. Key Points: Four year‐round North American regimes exhibit a clear dynamical footprint, supporting their interpretation as physically‐meaningfulThe regimes are persistent atmospheric states with a coherent lifecycle‐like temporal evolutionThe stronger the atmospheric flow projects onto the assigned regime, the more evident the above properties become [ABSTRACT FROM AUTHOR]

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process‐informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes. Process‐informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases. Plain Language Summary: Subseasonal rainfall forecasts provide alerts multiple weeks ahead. These forecasts present an opportunity to facilitate anticipatory actions yet are often unreliable to use when preparing for extreme weather. We develop a method to optimize rainfall forecasts by selecting individual members from a large ensemble of dynamic forecasting model outputs based on their ability to represent potential predictors of rainfall. We test our method on monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as key test cases. We select members from five contributing models of the C3S multimodel ensemble using regional predictors, including wind direction and sea surface temperatures (SSTs). Our results show improvements in the detection of low and high rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes like SSTs and is beneficial for operational forecasters who can leverage regional expertise of relevant rainfall‐generating processes to subsample better performing ensemble members for their regions. Key Points: Subsampling members using sea surface temperatures and zonal wind improves subseasonal ensemble rainfall forecasts in Central AmericaIn multiple months and locations mean squared error skill increases by 0.4 and extreme rainfall skill improves by 0.5 (Heidke skill)Process‐informed subsampling is useful because the models' representation of rainfall degrades as process error increases [ABSTRACT FROM AUTHOR]

Abstract Despite the significant socioeconomic implications in the link between atmospheric waviness and extreme weather events, future atmospheric waviness trends remain elusive due to uncertainties arising from diverse definitions and insufficient dynamical formalism in existing metrics. This study employs a local wave activity (LWA) metric, whose prognostic equation links wave activity changes to forcing mechanisms, to assess wintertime Northern Hemisphere waviness in ERA5 and HighResMIP data sets. The models generally exhibit high fidelity in reproducing observed waviness, while biases stem primarily from biases in the LWA source, low‐level meridional heat flux, which tend to improve with higher resolutions. Future projections exhibit reduction in LWA, primarily due to suppressed LWA generation, which is mitigated by higher‐resolution models. We found that both biases and reduction of the LWA source are closely associated with sensible heat fluxes from the ocean to the atmosphere, highlighting the potential impacts of resolving ocean currents.

Abstract The South Asian summer monsoon strongly modulates regional temperature and humidity. While extreme dry heat peaks in the pre‐monsoon season, recent literature suggests that extreme humid heat can continue to build throughout the monsoon season. Here we explore the influence of monsoon onset and subseasonal precipitation variability on the occurrence of extreme wet bulb temperatures (Tw) across South Asia. We find that extreme Tw events often occur on rainy days during the monsoon season. However, the influence of precipitation on Tw varies with the background climatology of surface specific humidity. In climatologically drier areas, positive Tw anomalies tend to occur when precipitation increases due to either early onset or wet spells during the monsoon. In contrast, in climatologically humid areas, positive Tw anomalies occur during periods of suppressed precipitation, including both delayed onset and dry spells during the monsoon.

Abstract Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process‐informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes. Process‐informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases.

A new procedure to obtain a longitudinally varying and slowly evolving atmospheric background state for the analysis of Rossby waveguides is described and discussed. The procedure is a rolling zonalization scheme, redistributing Ertel potential vorticity in a moving window to separate waves from the background. Waveguides are subsequently diagnosed from the gradient of the logarithm of potential vorticity. The effectiveness of the wave‐background separation, even in large‐amplitude conditions, is illustrated with reanalysis data. Established climatological‐mean waveguide structures are recovered from the rolling‐zonalized state in the limit of long‐term aggregation. Two contrasting episodes of Rossby wave packet propagation demonstrate how the evolution of waveguides derived from rolling zonalization can correspond to the development of superposed wave packets. The ability of the procedure to work with snapshots of the atmosphere provides new opportunities for waveguide research. Plain Language Summary: Rossby waves are meridional excursions of the jet stream, a strong band of wind in the extratropics. Stationary Rossby waves can cause extreme weather at the surface and traveling waves connect the weather of remote regions on the globe. Paths along which Rossby waves preferentially develop and travel are called waveguides. To detect the presence of a waveguide in atmospheric data, the waves have to be separated from their guiding atmospheric background state first. We introduce a new separation procedure for snapshots of the atmosphere that results in a slowly evolving and longitudinally varying background state. Our background state is a new source of local waveguide information, particularly in applications where no reliable information was available previously. Key Points: We construct a new atmospheric background state that is local in both space and timeWaveguide information can be extracted from the background state potential vorticity fieldOur scheme enables instantaneous waveguide analysis while also reproducing established waveguide patterns after long‐term aggregation [ABSTRACT FROM AUTHOR]

Weather system clustering provides a high‐level summary of regional meteorological conditions. Most quantitative clustering schemes focus on precipitation alone, which does not sufficiently describe the meteorological conditions driving hydroclimate variability. This study presents the Weather Anomaly Clustering (WAC‐hydro), which extends the existing capability of predicting weather systems to predicting hydroclimate variability. Focusing on both precipitation and temperature predictions, WAC‐hydro identifies 12 clusters of daily weather anomaly modes in the US Pacific Northwest Puget Sound region during 1981–2020. The influence of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on regional precipitation can be well approximated by their modulation on the weather clusters. Within each weather cluster, local factors such as topography only play a secondary role in the hydrologic variability. The weather clusters highlight two types of flood‐inducing regional weather conditions, one causing floods by inducing positive precipitation anomalies and the other causing floods through combined precipitation and temperature‐induced rain‐on‐snow effect. Plain Language Summary: Weather systems clustering (WSC) provides a high‐level summary of regional weather conditions, and they have wide applications in weather forecasting, climate analysis, disaster preparation and travel planning. Although many WSC algorithms have been developed, most focus on either phenomenological tagging (i.e., tagging each day as sunny/rainy/snow/fog/etc. type) or predicting a single meteorological variable (usually precipitation P) when optimized toward surface meteorological conditions. Given the importance of both P and temperature (T2) in driving land hydroclimatic variability and extreme, a WSC is developed and optimized for concurrent P and T2 prediction. The system (Weather Anomaly Clustering or WAC‐hydro) is demonstrated in a US Pacific Northwest watershed and identified 12 different daily weather clusters during the cold season from 1981 to 2020. These weather clusters feature unique combinations of P/T2 conditions, causing differing regional snowpack and runoff responses: one cluster causes more floods by enhancing P, while another causes floods through a combination of enhanced P and warm temperature induced rain‐on‐snow. Additionally, the weather clusters can link the regional P to well‐known modes of climate variability, suggesting that their modulations on regional hydroclimate variability can be estimated using their modulations on the weather systems. Therefore, WAC‐hydro can bridge large‐scale climate conditions to regional/local hydroclimatic conditions. Key Points: A new weather system clustering model is developed specifically for hydroclimate analysis and predictionTwelve weather systems in the Puget Sound region connect the regional hydroclimate to major modes of climate variabilityRegional cold season floods are tied to two weather systems, each highlighting unique flood mechanisms [ABSTRACT FROM AUTHOR]

Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra‐tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region‐specific adaptation strategies. Plain Language Summary: Global warming is changing how and when heavy rain and extreme weather events happen. It is important to understand these changes for planning and preparing for floods and other water‐related problems. In this study, we looked at long records of rainstorms to see how they have changed over time. We found that different regions have experienced different changes. During the second half of the 20th century, Africa and Asia were regions with the strongest seasonality. Over the 60 years we analyzed, seasonal clustering overall increased in Africa, while in Asia they became more spread out throughout the year. In Europe, North America and Australia, rainstorms were more spread out throughout the year but became slightly more concentrated. Most of the heavy rainstorms outside the tropics happened in summer, with a small shift in timing. These findings show how various regions have experienced different changes, which is important to take into account when planning and preparing for floods. Key Points: Global assessment of changes (1959–2021) in seasonality and timing of extreme daily precipitation occurrences using relative entropyPrecipitation extremes became more clustered in Africa and less clustered in AsiaShifts in timing of extreme precipitation by only a few days for most regions [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary: California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points: A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimesThis hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidenceThis new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events [ABSTRACT FROM AUTHOR]

La Niña persisted from 2020 to 2023, but its mechanisms are still unclear. In this study, atmosphere and ocean reanalysis and 100‐member initialized forecasts using a state‐of‐the‐art climate model were analyzed to identify factors contributing to the persistence of the first‐ to second‐year La Niña during 2020–2022. We found that North Pacific high pressure anomalies in the winter of 2020/2021 forced a negative phase of the Pacific meridional mode through the following spring, forming the broader structure of La Niña. The resultant broader La Niña pattern slowed down the recharge‐discharge process by Ekman transport, persisting La Niña. Ensemble forecast sensitivity analysis revealed that the meridional extent of La Niña explains its forecast spread, reaffirming the importance of La Niña spatial pattern. Advancing predictive understanding of 2020–2022 multi‐year La Niña can help to improve the extended seasonal forecast. Plain Language Summary: Almost 3 years have passed since the sea surface temperature in the central‐eastern equatorial Pacific became a cooler‐than‐normal in 2020. This event, called La Niña, can influence our lives through causing anomalous weather conditions globally. Therefore, it is important to know why the La Niña event has lasted so long. Using the computer simulation of climate, we repeated La Niña forecasts started from November 2020 a hundred times. We found that when the shape of La Niña was broader, the forecast was successful 1 year ahead. The shape of La Niña in 2021 depended on the atmospheric condition in the North Pacific. This study suggests that checking La Niña shape informs whether La Niña continues or declines. This knowledge may improve future climate forecasts. Key Points: The 100‐member ensemble forecasts initialized in November 2020 by MIROC6 captured prolonged La Niña in 2021/2022The forecast sensitivity analysis to initial states of ensemble members revealed a critical role of the North Pacific highNorth Pacific high in winter 2020/2021, causing negative North Pacific Meridional Mode spring, formed the spatially broader La Niña, resulting in long persistence [ABSTRACT FROM AUTHOR]

The Yangtze River Basin experienced one of the worst flash droughts on record during 2022 summer, but the relative contributions of precipitation deficit and high temperature to drought onset speed and intensity are difficult to separate due to the compounding feature of hot and dry extremes caused by persistent high‐pressure anomalies. Based on high‐resolution land surface model ensemble simulations with specified meteorological conditions that reproduce precipitation deficit and/or high temperature anomalies during 2022, we find the precipitation deficit is the dominant factor for triggering the flash drought. Meanwhile, high temperature further accelerates the decrease of soil moisture and intensifies drought condition by increasing evapotranspiration, which contributes 37% ± 14% (31% ± 15%) and 36% ± 11% (30% ± 15%) of the drought onset speed and intensity over upper reach (middle and lower reaches). Our study reveals the nontrivial role of high temperature in intensifying the 2022 Yangtze flash drought, which might be more significant in the warmer future. Plain Language Summary: As the extremely high temperature sweeping the Northern Hemisphere in the summer of 2022, an unprecedented flash drought broke out over the Yangtze River Basin (YRB), which caused a serious impact on the ecological environment, agricultural production, and even livelihood. It was ranked as one of the top 10 weather and climate extreme events of 2022 in China. In this study, we focused on the physical mechanism of the onset process of 2022 flash drought and quantified the contribution of precipitation deficit and high temperature through a set of high‐resolution land surface model simulations with specified climate anomalies. The results show that the unprecedented flash drought has a rapid onset speed and strong intensity, which is the result of the combined impact of precipitation deficit, high temperature, and strong radiation. The precipitation deficit played a dominant role in triggering the flash drought. In addition, the high temperature and strong radiation aggravated the drought conditions by increasing evapotranspiration and further accelerating the onset speed and enhancing the drought intensity. With the hot extremes being increasing in the future, flash droughts could be a great threat to food, energy and water security even in humid regions including the YRB. Key Points: The 2022 Yangtze flash drought broke out over upstream in July and downstream areas in August, with rapid onset speed and strong intensityPrecipitation deficit far exceeded the evapotranspiration excess for increasing the onset speed and intensity during drought onsetHigh temperature aggravated drought onset speed and intensity, with contributions of 37 (31)% and 36 (30)% for upstream (downstream) areas [ABSTRACT FROM AUTHOR]

Understanding eastward‐propagating mechanisms of the Madden–Julian Oscillation (MJO) is of great importance for the subseasonal prediction of extreme weather and climate worldwide. Using global satellite observations and reanalysis data, this study unravels that dual combinations of strong/weak westward‐ (ISOw) and eastward‐propagating intraseasonal oscillation (ISOe) can shape diverse MJO propagations documented previously using clustering analysis. The dry ISOw signals from the Central Pacific strengthen the leading suppressed convection over the Western Pacific (WP) and, on the contrary, weaken the moist ISOe convection over the Maritime Continent. Thus, when ISOe is weak over the WP, the strong (weak) dryness of ISOw likely causes a jump‐like (stand‐like) MJO mode. In contrast, a propagating MJO is supported when ISOe becomes strong over the WP, and a further strengthening of the ISOw dryness will presumably accelerate MJO; moreover, a weakening of ISOw might slow down the MJO speed. Plain Language Summary: Owing to the eastward propagation of the Madden–Julian Oscillation (MJO), tropical winds and precipitation usually oscillate in a broad life cycle of 20–100 days. The subseasonal (longer than 2 weeks but shorter than one season) prediction of extreme events, such as tropical cyclones, droughts, and heat waves, relies closely on an adequate understanding of the propagation mechanisms of MJO. Recently, clustering analysis has revealed four MJO propagation patterns, including stand, jump, slow, and fast modes. However, the underlying mechanisms of the origin of MJO propagation diversity are still elusive. Herein, we offer a novel explanation from the perspective of atmospheric westward‐propagation disturbances (WPDs). These WPDs, which first appeared over the Central Pacific, can cause stand‐ and jump‐like MJO modes when the eastward‐propagating 20–100‐day dry signals are weak over the Western Pacific. Otherwise, fast and slow MJO modes will be supported when the WPDs are further strengthened and weakened, respectively. These results highlight that the WPDs and their triggering place, that is, the Central Pacific, might serve as key ingredients for better understanding the MJO dynamics and predictability. Key Points: Dual combinations of strong/weak westward‐ and eastward‐propagating disturbances shape Madden–Julian Oscillation (MJO) diversityWestward‐propagating disturbances control the formation of stand and jump MJOs and distinguish fast and slow MJOsEastward‐propagating disturbances primarily help MJO cross the Maritime Continent barrier [ABSTRACT FROM AUTHOR]

As one of the major producers of extreme precipitation, mesoscale convective systems (MCSs) have received much attention. Recently, MCSs over several hotpots, including the Sahel and US Great Plains, have been found to intensify under global warming. However, relevant studies on the East Asian rainband, another MCS hotpot, are scarce. Here, by using a novel rain‐cell tracking algorithm on a high spatiotemporal resolution satellite precipitation product, we show that both the frequency and intensity of MCSs over the East Asian rainband have increased by 21.8% and 9.8% respectively over the past two decades (2000–2021). The more frequent and intense MCSs contribute nearly three quarters to the total precipitation increase. The changes in MCSs are caused by more frequent favorable large‐scale water vapor‐rich environments that are likely to increase under global warming. The increased frequency and intensity of MCSs have profound impacts on the hydroclimate of East Asia, including producing extreme events such as severe flooding. Plain Language Summary: Mesoscale convective systems (MCSs), accounting for more than half of the total rainfall in the East Asian rainband, frequently generate high‐impact extreme weather events, such as flooding. In the summer of 2020, large regions of East Asia suffered extensive flooding and damage. Therefore, understanding the long‐term changes of MCSs is crucial to gain insights into how extreme weather may change in the context of global warming. However, compared to several other MCS hotpots, the investigation of long‐term changes of MCSs is scarce over East Asia. Here, based on a high spatiotemporal resolution satellite precipitation product and a novel MCS tracking method, we find that MCSs have become more frequent and intense in the East Asian rainband and accounted for three quarters of the total rainfall increase during 2000–2021. It is further found that increases in atmospheric total column water vapor, which is mainly due to increased temperature caused by anthropogenic forcing, leads to more frequent large‐scale water vapor‐rich environments that are responsible for the intensification of MCSs. As water vapor increases with global warming, it is very likely that MCSs will continue to intensify in this region into the future. Key Points: Mesoscale convective systems (MCSs) have become more frequent and intense in the East Asian rainband over the past two decadesThe significant increase of MCS precipitation accounted for three quarters of the total rainfall increase during 2000–2021The increase of atmospheric total column water vapor, mainly driven by anthropogenic forcing, leads to more favorable environments for MCSs [ABSTRACT FROM AUTHOR]

The influence of the cross‐equatorial northerly surge (CES) on the eastward propagation of Madden‐Julian oscillation (MJO) during boreal winter is evaluated through the analysis of the column integrated moisture budget. Results show that the CES reinforces MJO's southward detour by increasing horizontal moisture convergence over the southern Maritime Continent (MC) region. Further analysis reveals that the zonal convergence by intraseasonal zonal wind anomalies acting upon background moisture is intensified in the presence of CES events, causing a stronger convective activity in the southern MC (SMC). The stronger moisture convergence in the SMC is associated with the CES‐induced intensification of low‐level northwesterly and westerly winds, which, in turn, strengthen zonal wind convergences and positive wind‐evaporation feedbacks onto the MJO convection. An improved process understanding of the link between the CES and MJO detours can help engender improvements in extreme weather forecasts and aid investigation biases in simulating MJO in climate models. Plain Language Summary: Madden‐Julian oscillation (MJO) is a tropical large‐scale atmospheric phenomenon characterized by an eastward propagating band of clouds and rainfall. Sometimes, during its journey over the Maritime Continent (MC), the MJO takes a southward detour. This detour can have significant impacts on the climate and weather patterns. In this study, we explore the potential impact of a cross‐equatorial northerly cold surge (CES) on the magnitude of the MJO detour over the MC. We find that the magnitude of the MJO's southward detour becomes stronger in the presence of CES events. The CES increases northwesterly and westerly winds, which in turn enhances zonal moisture convergence and strengthens positive wind‐evaporation feedback onto MJO convection. A better understanding of the relationship between the CES and the MJO can lead to improvements in extreme weather forecasting over the MC and other regions, and aid in investigating biases in simulating MJO propagation in global climate models. Key Points: Cross‐equatorial surge (CES) amplifies Madden‐Julian oscillation (MJO)'s southward detour during the active Australian monsoon season (December–March)Zonal moisture convergence by CES wind acting upon the background moisture increases MJO convection over the southern Maritime ContinentCES‐induced intensification of low‐level northwesterly and westerly winds enhances positive wind‐evaporation feedback onto MJO convection [ABSTRACT FROM AUTHOR]

Rainfall high on the Greenland Ice Sheet is an emerging phenomenon with consequences for the thermal and structural makeup of the surface layer. We document changes to Greenland's firn column due to a 4‐day cold‐season warm/rain event. Heavy precipitation occurred with a sudden 30°C increase in air temperature, reaching 0°C at 2,000 m elevation. Thermistor strings within the firn layer across a 35 km transect show rapid warming of 6°–23°C reaching depths of 2–10 m. Antecedent conditions governed the magnitude, duration, and depth‐distribution of sensible and latent heat added to the firn column. Heat fluxes from the firn layer required up to 8 weeks to recover to baseline, a significant fraction of the winter period. The amount of liquid water refrozen in the firn column was ∼20%–100% of the prior summer demonstrating the impact of extreme weather events on the ice sheet's evolution and runoff characteristics. Plain Language Summary: Based on measurements from a network of sensors installed on the Greenland Ice Sheet, we document changes in the firn layer (snow in transition to ice) due to a late Autumn extraordinary warm/rain event. After an extended period of cold‐season cooling of the firn, the event caused the firn layer to warm by 6°–23°C, mainly in one day, as rainwater and surface melt penetrated up to 6 m depth and refroze. The water added to the firn layer during this extreme weather event was ∼25%–100% of what was added during the entire previous summer melt season. The unusual timing of water input during the cold‐season likely altered the firn differently than summer input from melt or rain. Our results demonstrate that only a minor increase in the frequency and magnitude of similar rain events has potential for outsized impact on the thermal and density transformation of Greenland's firn layer which has implications for future runoff processes. Key Points: A cold‐season rainstorm on the Greenland Ice Sheet demonstrates outsized impact of extreme weather events on evolution of the firn layerFirn layer temperature quickly increased by up to 23°C, altering heat fluxes and requiring up to 2 months to thermally recoverThe liquid water refrozen within the firn layer was ∼20%–100% of the amount of water refrozen during the previous summer [ABSTRACT FROM AUTHOR]

The abrupt alternation between hot and wet extremes can lead to more severe societal impacts than isolated extremes. However, despite an understanding of hot and wet extremes separately, their temporally compounding characteristics are not well examined yet. Our study presents a comprehensive assessment of successive heat‐pluvial and pluvial‐heat events globally. We find that these successive extremes within a week occur every 6–7 years on average within warm seasons during 1956–2015, about 15% more often than would be expected by chance, and that they have a significant increase in frequency of about 22% per decade due to warming. We further investigate the role of vapor pressure deficit (VPD) and find that heat‐pluvial (pluvial‐heat) events are linked to negative (positive) VPD anomalies. Our results are statistically significant based on moving‐blocks bootstrap resampling and field significance tests, highlighting these methods' importance in robustly identifying compound events under autocorrelation and multiple‐testing conditions. Plain Language Summary: In recent years, the world has experienced various clustered weather and climate extremes, which are highly disruptive to humans and society. However, current knowledge on the risk of successive occurrence of hot (humid heat, including the effects of both temperature and humidity) and wet (pluvial flooding, usually caused by extreme rainfall) extremes remains unclear. In this study, we present a comprehensive assessment of the two types of interacting hot and wet extremes: humid heat extremes followed by pluvial flooding (heat‐pluvial) and extreme pluvials followed by humid heat (pluvial‐heat). We find that these events have increased significantly in most regions of the world for the last three decades, which can be associated with the warming effect. Importantly, we identify that the vapor pressure deficit plays an important but varying role in the abrupt alternation between heat and pluvial events. We emphasize the importance of using reliable statistical tests to ensure the validity of the results for complex compound events. Our analysis highlights the need for policymakers and stakeholders to develop adaptation strategies to cope with overlapping vulnerabilities due to compound hot and wet extremes, especially in areas prone to both such as West Australia, South America and Sub‐Saharan Africa. Key Points: Temporally compounding heat and pluvial events occur about 15% more often than would be expected by chanceIncreases in hot‐wet compound events have largely been linked to warmingVapor‐pressure‐deficit anomalies are a signature of heat‐pluvial versus pluvial‐heat sequences, a conclusion drawn from field significance tests [ABSTRACT FROM AUTHOR]

Extreme events have become more frequent in the Northern Hemisphere mid‐latitudes in recent decades, hugely impacting the ecosystem and society. The quasi‐resonance amplification (QRA) of planetary waves is considered one of the dynamic mechanisms leading to extreme weather. However, the specific impact of resonant waves caused by QRA on surface heat extremes is still an open issue. Here we show that the QRA of planetary waves leading to heat extremes is closely related to double jets, accompanied by the enhancement of atmospheric blocking and the weakening of planetary wave escaping into the stratosphere. Our results illustrate the important role of mid‐latitude planetary waves with zonal wavenumber m = 6–8 caused by QRA in the increasingly frequent heat waves in recent years, as it can increase the coverage area and occurrence frequency of heat extremes in mid‐latitudes and show a significant impact on preferred regions, especially in Eurasia. Plain Language Summary: In the summer of 2022, the Northern Hemisphere mid‐latitudes were affected by widespread, intense heat waves that developed more quickly than climatologists had expected, hugely impacting the ecosystem and society. Understanding the extent and causes of extreme heat can help local authorities better prepare for the extreme weather that may occur in the near future. Planetary wave, also known as Rossby wave, manifested as a large‐scale oscillation of the midlatitude atmospheric flow, usually forms a snaking shape over the Earth, leading to persistent surface weather systems and often resulting in extreme events. Existing studies have shown that, planetary wave is one of the important dynamic mechanism behind heat waves. At certain frequencies and wavelengths (short‐wavelength), this circumglobal waves disturbance can be significantly amplified through resonance, resulting in heat extremes. Our work indicates that resonant wave plays an increasingly important role in heat events over Northern Hemisphere mid‐latitudes. The long‐term changes in resonant wave trend deserve continuous attention, because an improved understanding of planetary waves behind extreme events can help assess future wave activity's potential social and economic effects. Key Points: The influence of resonant waves on heat extremes in the Northern Hemisphere mid‐latitude summer is increasing, especially in EurasiThe occurrence of resonant waves is closely related to double jets, accompanied by the enhancement of atmospheric blockingThe occurrence of resonant waves is accompanied by the weakening of the upward‐propagating planetary waves [ABSTRACT FROM AUTHOR]

The Warm Arctic‐Cold Eurasia pattern in recent decades has attracted extensive attention and has been partly attributed to rapid sea‐ice loss; however, such a link has been unstable over the past century. Here, we use centennial reanalyzes to investigate the multi‐decadal changes in the linkage between autumn Barents‐Kara sea‐ice and winter Eurasian temperature during the 20th century. Results show a negative Arctic‐Eurasian connection in 1901–1945 (P1), an inconspicuous connection in 1946–1985 (P2), and a significantly positive correlation in 1986–2013 (P3), with distinct stratospheric and tropospheric pathways mechanisms. The contrasting relationships between P1 and P3 are likely due to the different magnitudes of sea‐ice loss and the predominant roles of the atmosphere or sea‐ice in driving different Eurasian temperature effects. As further verification, the best group of Coupled Model Intercomparison Project simulations, which captured the realistic stratospheric pathway in recent decades, can reproduce the historical decadal changes in the Arctic‐Eurasian linkage. Plain Language Summary: Central Eurasia has experienced more frequent cold winters and extreme weather events in recent years. Many mechanisms have been proposed for the frequent occurrence of freezing winters. Both observational and modeling studies suggested that the rapid sea‐ice decline has influenced the increase in cold temperature events. Melting autumn sea‐ice leads to a weak polar vortex and subsequent cold winter in Eurasia. This correlation and physical linkage are verified by observation and model simulation after 1979 because the sea‐ice can be observed in the satellite era. In this study, we primarily examined the warm Arctic‐cold Eurasia pattern that existed and changed over the past century. Then we investigate the decadal change in the relationships between Barents‐Kara sea‐ice and the winter central Eurasian temperature on a centurial time‐scale which shows diverse features in different periods. An analysis of the model data set from Coupled Model Intercomparison Project (CMIP6) models confirms that the Arctic‐Eurasian relationship varies in three periods, and the models which can capture the realistic physical process in the stratosphere in recent three decades are able to reproduce the main feature of the evolution in the Arctic‐Eurasian relationship over the entire 20th century. Key Points: The linkage between autumn Barents‐Kara sea ice and winter Eurasian temperature showed decadal changes in the 20th century and is only significant in recent decadesThe stratospheric pathways and winter Ural Blocking activities can serve as bridges to link autumn sea ice and winter Eurasian temperature variabilityCoupled Model Intercomparison Project models with realistic stratospheric pathways can capture the observed multi‐decadal evolution of autumn sea ice‐winter Eurasian temperature linkage [ABSTRACT FROM AUTHOR]