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In climate science and applications, the term "metric" is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are "fit for purpose." Metrics can also provide a valuable reference point for co‐production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. This paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision‐making. This article is categorized under:Climate Models and Modeling > Knowledge Generation with ModelsThe Social Status of Climate Change Knowledge > Climate Science and Decision MakingAssessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change [ABSTRACT FROM AUTHOR]

The East Asian winter monsoon (EAWM) is one of the most important climate systems during boreal winter. It has a large meridional extent and provides a vital bridge between the mid‐to‐high latitudes and the tropics. This paper reviews the latest research on the climate variability of the EAWM, with particular emphasis on the associated processes of extratropical–tropical interaction. Some new results have been achieved in understanding the variation of the EAWM on interdecadal, interannual, and intraseasonal time scales, such as the interdecadal strengthening in the early 21st century, the unstable impact of El Niño–South Oscillation (ENSO) on the EAWM, and the intraseasonal strong cold events in East Asia. In addition, understanding of the extratropical–tropical interactions related to the EAWM has improved in several aspects, including the impacts of the EAWM on ENSO‐related climate effects, and the variation of regional Hadley circulation over the western Pacific and its climate impacts in the Asia–Australia region. Moreover, recent work has systematically evaluated the capacity of climate models to simulate the EAWM, including its climatology, interannual to interdecadal variations, and its relationship with ENSO. Finally, some scientific issues regarding our understanding of the EAWM are proposed for future investigation. [ABSTRACT FROM AUTHOR]

The often neglected Guy Stewart Callendar (a steam engineer, for whom climate science was a hobby) was the first to bring together measurements of carbon dioxide concentration with how much the world had warmed from the late-nineteenth century in two key papers in I QJRMS i (in 1938 and 1961). David gave a brief history of climate science and how this has impacted UK Government Policy over the period since the late 1980s. Joanna also noted those who were sceptical of the importance of carbon dioxide (Knut Angstrom who erroneously stated that the carbon dioxide absorption bands were swamped by water vapour, and later ideas in the 1950s that carbon dioxide was being taken out of the atmosphere by the oceans much more than in reality). [Extracted from the article]

Climate models struggle to produce sea surface temperature (SST) gradient trends in the tropical Pacific comparable to those seen recently in nature. Here, we find that the magnitude of the cloud‐SST feedback in the subtropical Southeast Pacific is correlated across models with the magnitude of Eastern Pacific multi‐decadal SST variability. A heat‐budget analysis reveals coupling between cloud‐radiative effects, circulation, and SST gradients in driving multi‐decadal variability in the Eastern Pacific. Using this relationship and observed feedback estimates, we find that internal Eastern Pacific SST variability is underestimated in most models. Adjusting for model bias increases the likelihood of generating a cooling trend at least as large as observations in preindustrial control simulations by ∼ ${\sim} $56% on average. If models underestimate climate "noise," as our results suggest, this bias should be accounted for when attributing the relative importance of forced versus unforced changes in the climate. Plain Language Summary: In recent decades, observed sea surface temperatures (SSTs) have cooled in the eastern tropical Pacific and warmed in the western tropical Pacific. Historical simulations using state‐of‐the‐art climate models fail to reproduce this pattern. We find that the feedback between low‐lying clouds and SSTs is related to the magnitude of naturally occurring SST variability in the Southeast Pacific. On average, climate models that have too weak a cloud‐SST feedback in the Southeast Pacific underestimate the likelihood of multi‐decadal cooling in the eastern Pacific in preindustrial simulations. Our results suggest that biases in cloud feedbacks may be causing models to underestimate internal SST variability. Key Points: Multi‐decadal Southeast Pacific sea surface temperature trends are related to the strength of the subtropical cloud feedbackCloud radiative effects amplify multi‐decadal Pacific sea surface temperature trends by impacting circulation and surface energy fluxesCorrecting model biases in cloud feedback raises the likelihood of internally producing Southeast Pacific cooling as large as observations [ABSTRACT FROM AUTHOR]

Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature‐based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long‐standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer‐scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate. Plain Language Summary: Terrestrial ecosystems provide a natural solution to planetary warming by storing carbon, dissipating surface heating through evapotranspiration, and other processes. That forests, in particular, influence climate is a centuries‐old premise, but its potential for planetary stewardship has not been realized. In an acrimonious controversy spanning several centuries, managing forests to purposely change climate was advocated, legislated, and resoundingly dismissed as unscientific. Similar intellectual bias is evident in today's Earth system science and the associated Earth system models, which are the state‐of‐the‐art models used to inform climate policy. The popular characterization of Earth system science lauds its interdisciplinary melding of physics, chemistry, and biology, but the models emphasize the physics and fluid dynamics of the atmosphere and oceans and present a limited perspective of terrestrial ecosystems in the Earth system. Ecologists studying the living world increasingly have a voice in Earth system science as we move beyond the physical basis for climate change to Earth system prediction for planetary stewardship. As we once again look to forests to solve a climate problem, we must surmount the disciplinary narrowness that failed to answer the forest‐climate question in the past and that continues to limit the interdisciplinary potential of Earth system science. Key Points: Nature‐based climate solutions have been advocated for centuries, but have been distorted by academic bias and colonialist prejudiceEarth system science, while recognizing the climate services of the biosphere, has a geophysical bias in interdisciplinary collaborationTo realize the potential for planetary stewardship, Earth system models must embrace the living world equally with the fluid world [ABSTRACT FROM AUTHOR]

Clouds are a key player in the global climate system, affecting the atmospheric water and energy budgets, and they are strongly coupled to the large-scale atmospheric circulation. Here, we examine the co-variability of the atmospheric energy and water budget imbalances in three different global model configurations-radiative-convective equilibrium, aqua-planet, and global simulations with land. The gradual increase in the level of complexity of the model configuration enables an investigation of the effects of rotation, meridional temperature gradient, land-sea contrast, and seasonal cycle on the co-variability of the water and energy imbalances. We demonstrate how this co-variability is linked to both the large-scale tropical atmospheric circulation and to cloud properties. Hence, we propose a co-variability-based framework that connects cloud properties to the large-scale tropical circulation and climate system and is directly linked to the top-down constrains on the system--the water and energy budgets. In addition, we examine how the water and energy budget imbalances co-variability depends on the temporal averaging scale, and explain its dependency on how stationary the circulation is in the different model configurations. Finally, we demonstrate the effect of an idealized global warming and convective aggregation on this co-variability. Plain Language Summary Understanding the link between clouds and the atmospheric circulation is considered as a major challenge in climate science. Both atmospheric circulation and clouds are intimately linked to Earth's water and energy budgets. We show that looking at how the residuals of these budgets depend on each other and on cloud properties can provide a new framework with which to study the properties of clouds, circulations, and their respective impacts on the climate system. In this paper, we present this framework, and demonstrate its usefulness for a range of different climate model complexities. We show examples of how the framework could be used to investigate the effects of climate change on clouds and circulation. Finally, we present an application for convective aggregation (the clustering of deep convective clouds in idealized climate models). [ABSTRACT FROM AUTHOR]

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two existing approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics but fails to enhance the sampling of sudden, transient extremes; and "ensemble boosting," which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset, following the approach of ensemble boosting. Early splitting requires a rejection step that reduces efficiency, but it is critical for amplifying and diversifying simulated events in tests with the Lorenz‐96 model, for which we demonstrate improved sampling of extreme local energy fluctuations by approximately a factor of 10 relative to direct sampling. Our method is related to previous algorithms, including subset simulation and anticipated AMS, but is distinctly tailored to handle bursting events caused by chaotic traveling waves. Our work makes progress toward the goal of efficiently sampling such transient local extremes in atmospheric models. Plain Language Summary: What is the strongest rainstorm that we can expect in a given thousand‐year period? To augment the available ∼100 years of historical data and to account for climate change, computer simulations are a useful, but expensive, tool to answer such questions. A model must run for many millennia to deliver an answer with statistical confidence. Rare event algorithms provide a promising alternative simulation protocol, in which an ensemble of short simulations is biased to produce more extreme events and reweighting is used to correct for the bias when calculating statistics. However, a classical rare event algorithm fails when the events of interest are short and "bursty" (like heavy rainstorms) instead of long and slow‐moving (like anomalously hot summers). We modify the rare event algorithm to make it amenable to events resembling heavy precipitation in an idealized dynamical system with chaotic traveling waves. Key Points: Rare event algorithms may help address the challenge of simulating extreme weather events and quantifying their probabilityWhen the event of interest is sudden and transient, perturbed ensembles diversify too slowly for standard rare event algorithms to workUsing the Lorenz‐96 model as a prototype for midlatitude weather, we use early perturbation and a rejection step to gain a speedup [ABSTRACT FROM AUTHOR]

Emulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation. Plain Language Summary: Emulators are simplified climate models that can be used to rapidly explore climate scenarios—they can run in less than a minute on an average computer. They are key tools used by the Intergovernmental Panel on Climate Change to explore the diversity of possible future climates. Data‐driven emulators use advanced machine learning techniques to produce climate predictions that look very similar to the predictions of complex climate models. However, they are not easy to interpret, and therefore to trust in practice. In this work, we introduce FaIRGP, a data‐driven emulator based on physics. The emulator is flexible and can learn from data to improve its predictions, but is also grounded on physical energy balance relationships, which makes it robust and interpretable. The model performs well in predicting future global and local temperatures under realistic future scenarios, outperforming purely physics‐driven or purely data‐driven models. Further, the probabilistic nature of our model allows for mathematically tractable uncertainty quantification. By gaining trust in such a data‐driven yet physically grounded model, we hope the climate science community can benefit more widely from their potential. Key Points: We introduce FaIRGP, a Bayesian machine learning emulator for global and local mean surface temperatures that builds upon a physically based simple climate modelThe model improves upon both purely physically‐driven and purely data‐driven baseline emulators on several metrics across realistic future scenariosThe model is fully mathematically tractable, which makes it a convenient and easy‐to‐use probabilistic tool for the emulation of surface temperatures, but also for downstream applications such as detection and attribution or precipitation emulation [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

Dvorak technique inventor dies Pioneering tropical meteorologist Vernon Dvorak (Figure 2) passed away in September, leaving behind a legacy of vastly improved tropical cyclone intensity estimation which for decades has been a mainstay of tropical cyclone forecasting. Reading University meteorology building renamed The University of Reading's Meteorology building has been renamed after one of its most distinguished Professors, Sir Brian Hoskins (Figure 3). This month we are delighted to celebrate the 250th anniversary of the birth of Luke Howard, "namer of clouds"!. [Extracted from the article]

Classifications by self‐organizing maps (SOMs) have been used to study links between extreme weather events and atmospheric circulation. However, SOMs may often provide a too generalized image of circulation, which is too coarse to properly resolve meteorological extremes. Several approaches have been used to circumvent this limitation, including an increase in the number of nodes and/or relaxation of the stress on topological ordering of nodes, through adjusting the neighbourhood radius. Here, we (1) test whether weighting of training fields based on extremity of their circulation could lead to SOMs that would better represent circulation extremes without impairing the topological ordering of nodes, and (2) we investigate whether Sammon maps (SM), which have so far been limited to validation of SOMs in climatology, could provide a viable alternative to SOMs in studying weather extremes. We show that removing a certain percentage of circulation fields closest to the climatological mean has a greater effect on the representation of circulation extremes than more than doubling the SOM array size. Additionally, this step allows for decreasing the neighbourhood radius, the parameter responsible for topological ordering, which would not otherwise be possible without impairing the topology of the map. The SOMs are compared with two newly developed methods that discretize SMs based on arbitrarily chosen and distribution‐based bins. We demonstrate the usefulness of both methods in climate model validation and show that both methods are able to capture circulation variability in its extremes in more detail than the SOMs. The distribution‐based method does not suffer from sample‐size issues and outperforms all other classifications in extracting ERA5 patterns leading to extreme temperature minima over central Europe. Our results suggest that classifications based on SM could be a viable alternative to SOMs when SOMs lack the ability to discriminate circulation outliers leading to temperature extremes. [ABSTRACT FROM AUTHOR]

An anomalous signal‐to‐noise ratio (also called the signal‐to‐noise paradox) present in climate models has been widely reported, affecting predictions and projections from seasonal to centennial timescales and encompassing prediction skill from internal processes and external climate forcing. An anomalous signal‐to‐noise ratio describes a situation where the mean of a forecast ensemble correlates better with the corresponding verification than with its individual ensemble members. This situation has severe implications for climate science, meaning that large ensembles might be required to extract prediction signals. Although a number of possible physical mechanisms for this paradox have been proposed, none has been universally accepted. From a statistical point of view, an anomalous signal‐to‐noise ratio indicates that forecast ensemble members are not statistically interchangeable with the verification, and an apparent paradox arises only if such an interchangeability is assumed. It will be demonstrated in this study that an anomalous signal‐to‐noise ratio is a consequence of the relative magnitudes of the variance of the observations, the ensemble mean, and the error of the ensemble mean. By analysing the geometric triangle formed by these three quantities, and given that for typical seasonal forecasting systems both the correlation and the forecast signal are relatively small, it is concluded that an anomalous signal‐to‐noise ratio should, in fact, be expected in such circumstances. [ABSTRACT FROM AUTHOR]

In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large‐scale oceanic circulation. This stochastic framework arises from a decomposition of the Lagrangian velocity into a smooth‐in‐time component and a highly oscillating noise term. One important characteristic of this random model, without any external forcing and damping, is that it conserves the total energy of the resolved flow for any realization. The proposed stochastic formulation is successfully implemented in a well established multi‐layered quasi‐geostrophic dynamical core. The empirical spatial correlation of the unresolved noise is calibrated from the eddy‐resolving simulation data. In particular, a stationary correction drift can be introduced in the noise through Girsanov transformation. This non‐intuitive term appears to be important in reproducing on a coarse mesh the eastward jet of the wind‐driven double‐gyre circulation. In addition, a projection method has been proposed to constrain the noise to act along the iso‐surfaces of the vertical stratification. The resulting noise enables us to improve the intrinsic low‐frequency variability of the large‐scale current. Plain Language Summary: Accurate numerical simulation of the ocean and the atmosphere is important for understanding complex patterns in climate science. Unfortunately, the high‐dimensional geophysical fluid dynamics contain a wide range of spatio‐temporal scales with complex interactions. In order to maintain affordable computational time, only large‐scale representations of the geophysical flows of interest are simulated. However, the effect of the unresolved scales on the resolved component must be carefully taken into account to minimize the errors of truncation. In the present work, we stick to a specific stochastic formulation that represents the physical principles of the resolved large scales and parameterizes the statistical properties of the unresolved small scales. Such random formulation is successfully tested in numerical simulations of a simplified ocean mesoscale model, in which the structure of the unresolved flow is calibrated from a finite sequence of reference data. The resulting model enables to represent accurately the statistics of the resolved flow component. In addition, numerical results show that an adequate modeling of the unresolved dynamics leads to a significant improvement of the intrinsic variability for the large‐scale ocean models. These findings are important for future developments of ocean and atmosphere climate models. Key Points: A stochastic transport formulation is used to parameterize the effects of the unresolved eddies on the resolved flowThe structure of unresolved flow is calibrated from data and a projection method is proposed to parameterize its dynamicsThe proposed random models on coarse resolutions enable to reproduce the eastward jet and the intrinsic variability of ocean wind‐driven circulation [ABSTRACT FROM AUTHOR]

Climate models are widely used to project future climate changes and analyze underlying mechanisms. However, it remains unknown whether the earlier precipitation projections match the subsequent observations in recent decades, which is the scientific basis for the confidence of precipitation projections and associated impacts. Here, we find models in the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) skillfully project the subsequent climatological changes in global mean land precipitation for 2002–2020, 2008–2020, and 2014–2020 (relative to 1980–1999 climatology) from several to 10 years ahead, respectively. Skillful regional projections are mainly found in the northern mid to high latitudes. IPCC models are less skillful in projecting subsequent changes in land precipitation at regional scales than at global scales, which is likely to be at least partly explained by the lower signal‐to‐noise ratio. Plain Language Summary: As an important tool, climate models are widely used to project future climate changes and analyze underlying mechanisms. The precondition of this is that the projections of climate models are credible. The most convincing way of validating the credibility of climate projections is to quantify the extent to which past projections agree with subsequent observations. Here, we provide a comprehensive analysis on the skill of past climate models in projecting the subsequent precipitation changes over global land. Results show that models have robust skills in projecting the subsequent climatological changes in global mean land precipitation from several to 10 years ahead. Relative to global scale land, the models are less skillful in projecting the subsequent changes in precipitation at regional scale, although they exhibit some skills in northern mid‐ to high‐latitudes. Key Points: Models show robust skills in projecting the subsequent climatological changes in global mean land precipitation from several to 10 years aheadRelative to global scale, models are less skillful in projecting the subsequent climatological changes in precipitation at regional scaleThe near‐term precipitation projections are similar under various emission scenarios [ABSTRACT FROM AUTHOR]

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME‐mean) at monthly and seasonal time scales for the historical simulation over the period 1988–2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME‐mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME‐means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS‐g3, IPSL‐CM6A‐LR, and IPSL‐CM6A‐LR‐INCA, and one model in consecutive dry day (CDD) simulation, MPI‐ESM‐1‐2‐HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action. [ABSTRACT FROM AUTHOR]

The increasing awareness of climate change requires accurate, reliable and timely information on possible precipitation (PRE) changes to build climate resilience. This study uses the Coupled Model Intercomparison Project phase 6 (CMIP6) data to examine the effectiveness of bias correction on simulated historical mean and extreme PRE, and investigates the projected changes in extreme PRE events over southern Africa (SAF). Quantile mapping on a gamma distribution bias correction method is applied to the historical and projected CMIP6 data, with the Global Precipitation Climatology Centre (GPCC) PRE dataset as reference data. The projection analyses are conducted for the wet (December–March) and dry (May–October) seasons for two shared socioeconomic pathway (SSP) scenarios: SSP2‐4.5 and SSP5‐8.5. The results affirm that the bias correction significantly reduces (by more than 90%) the biases in the modelled mean and extreme PRE. The projected extreme PRE shows a general drying pattern over SAF. In the wet season, wet days (R1mm) are projected to decrease by 1 and 2 days by the end of the 21st century for SSP2‐4.5 and SSP5‐8.5, respectively, while very heavy PRE days (R20mm) show a slight upward pattern over SAF, reaching 0.1 and 0.4 days for the two scenarios. In contrast, the dry season exhibits a more pronounced drying tendency, with a consistent decrease in R1mm of 2 and 0.1 days by 2100 for SSP2‐4.5 and SSP5‐8.5, respectively. R20mm is projected to decrease by 4 and 0.2 days for SSP2‐4.5 and SSP5‐8.5, respectively. This implies that SAF is expected to get drier during the dry season while the PRE intensity during the wet season is projected to increase. [ABSTRACT FROM AUTHOR]

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea‐surface salinity and sea‐level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea‐ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere‐ocean‐ice interaction occurs. One‐year‐long sea‐ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea‐ice concentration from 2011 to 2019 is calibrated by trend‐adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea‐ice edge raw forecast skill is within the range of operational global subseasonal‐to‐seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea‐ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations. Plain Language Summary: Ocean data sparseness and systematic model errors pose problems for the initialization of coupled seasonal forecasts, especially in polar regions. Our global forecast system follows a seamless approach with refined ocean resolution in the Arctic. The new version presented here features higher computational efficiency and utilizes more ocean and sea‐ice observations. Ice‐edge forecasts outperform a climatological benchmark for about 1 month, comparable to established systems. Key Points: We describe an upgrade of the AWI Coupled Prediction System with new ocean and atmosphere models and more observations assimilatedIndependent evaluations show advances in the new version on the analysis of the sea‐ice and ocean states against the old oneCalibrated sea‐ice edge forecasts outperform a climatological benchmark for around 1 month in both hemispheres [ABSTRACT FROM AUTHOR]

The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the "climatic impact‐driver" (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as "physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions." We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities. Plain Language Summary: Climatic impact‐drivers (CIDs) are climate conditions that affect the things we care about in nature and society. We deepen the motivation and definitions that allowed the Intergovernmental Panel on Climate Change to identify 33 distinct CID categories including extreme heat, hydrological drought, severe wind storm, permafrost, relative sea level, marine heatwaves, and air pollution weather. Each CID category may be analyzed with specific indices that inform adaptation, mitigation and risk management. The CID Framework allows us to avoid universally labeling a climate condition as a "hazard," recognizing that the same physical condition may be detrimental for some and beneficial or inconsequential for others. This approach allows climate scientists to engage with impacts and risk experts to target specific tolerance thresholds that are system‐ and sector‐dependent. This more comprehensive description of the CID Framework provides a practical foundation for climate research, climate and impact model development, risk assessments and climate service product creation. Key Points: Deepens explanation of Climatic Impact‐Driver (CID) Framework utilized in Intergovernmental Panel on Climate Change Sixth Assessment ReportsDistinguishes practical CID types and categories that allows climate information to target conditions that affect the things we care aboutNeutral Framework does not pre‐judge beneficial, detrimental or neutral outcomes which are system‐ and sector‐dependent [ABSTRACT FROM AUTHOR]

The ability of regional climate models (RCMs) to accurately simulate the current climate is increasingly important for impact assessments over Southeast Asia (SEA), identified as one of the world's most vulnerable regions to climate change. In this study, we evaluate the performance of a set of regional high‐resolution simulations from the Coordinated Regional Climate Downscaling Experiment‐SEA (CORDEX‐SEA) in simulating rainfall over the region. Simulations of the 1982–2005 seasonal mean climatology of daily precipitation and precipitation distribution over land are compared to observations from different sources (i.e., in situ‐based and satellite‐based). We also evaluate to what extent the precipitation distribution in RCMs is closer to observations than their associated forcing global climate models (GCMs). Observational estimates of precipitation over SEA have large uncertainties, making the model evaluations complicated. Despite these difficulties, our results highlight that RCMs can reproduce some complexities in the spatial distribution of seasonal rainfall but generally have a larger wet bias than GCMs. This is particularly true for the extremes in which RCMs show a large overestimation of rainfall intensity. There are some precipitation quantiles and grid points in which RCMs show limited reductions in biases compared to observations, but there is no consistency across all simulations and RCMs are generally further away from observations than their forcing GCMs. We find that greater intensity in RCMs over CORDEX‐SEA compared to their associated forcing GCMs is firstly associated with the increased supply of moisture from both local and large‐scale sources. Second, a widespread increase in convective precipitation is found across the region in RCMs. Our findings suggest that a model's ability to simulate precipitation over the region relies more on the RCM setup itself (e.g., parameterization scheme), rather than its forcing GCM. This should be considered when assessing the reliability of RCM precipitation simulations for future projections. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their variability has been linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, and Pacific‐North America pattern. Understanding and improving simulation of this linkage can provide the potential to predict ARs at subseasonal‐to‐decadal timescales. Up to now, the extent to which climate model simulations of ARs are dependent on model resolutions has not been fully assessed. Here, we compare and evaluate ARs in a pair of high‐resolution (HR) and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results show that within this CESM framework, climatological AR strength and associated precipitation are severely underestimated by LR compared to the observations, and their relationships with major modes of climate variability are also poorly reproduced. These deficiencies in LR are alleviated to a significant extent in HR. Using a linear regression analysis, we show that HR is able to capture with high fidelity the observed relationships between ARs and major climate modes in the Northern Hemisphere at seasonal‐to‐decadal time scales. These results suggest that the use of HR CESM may potentially lead to an improved forecast skill of ARs at season‐to‐decadal time scales. Plain Language Summary: Atmospheric rivers (ARs) can trigger extreme rainfall and snowfall events, causing high risk of flooding. Meanwhile, they are sometimes responsible for the occurrence and break of drought events. So, it is of great importance for weather and climate forecast models to accurately represent ARs. Studies have shown that AR variability is linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, Pacific‐North America pattern, providing the potential for predicting ARs at subseasonal‐to‐decadal timescales. In this study, we examine the impact of climate model resolution on AR simulations and their relationship with major climate modes by comparing a pair of high‐resolution and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results reveal major deficiencies in LR that include underestimated climatological AR strength and associated precipitation, and inaccurate representations of ARs relationships with climate modes. Significant improvements are observed by increasing CESM horizontal resolution, suggesting a path for more accurate prediction of ARs. Key Points: Simulated atmospheric rivers (ARs) are dependent on horizontal resolutions of coupled Community Earth System Model (CESM)High‐resolution (HR) CESM simulates more realistic ARs' strength and associated rainfall than low‐resolution (LR)ARs' response to large‐scale climate modes is better simulated in HR CESM than in LR CESM [ABSTRACT FROM AUTHOR]

The evolution of hydroclimate in East Asia during the Pliocene—a potential analog of the future greenhouse climate—remains highly uncertain. Here we reconstruct changes in the hydroclimate of Northern China during this epoch. Our results reveal previously undocumented severe drought conditions that persisted through the early Pliocene with a significantly greater magnitude than suggested by other records. Using a broad hierarchy of climate simulations, we find that reduction in the Pacific meridional sea surface temperature (SST) gradient has particularly strong impacts on precipitation over Northern China, which suggests its key role in maintaining drought conditions during the early Pliocene. The weaker Pacific meridional SST gradient causes the weakening of the East Asian summer monsoon precipitation over this region via a reduction of atmospheric northward moisture transport from the ocean, which enhances aridity inland. Our results highlight the fundamental control of the tropical ocean on the extra‐tropical hydrological cycle. Plain Language Summary: Understanding East Asian rainfall changes with global warming is an important issue of climate sciences. The Pliocene epoch provides a potential analog to the future greenhouse climate. Here, for the first time we reconstruct high‐resolution aridity changes over the Chinese Loess Plateau (CLP) in northern East Asia during the Pliocene. Our results indicate persistent drought conditions over CLP during the early Pliocene. Analyzing a broad range of climate model simulations, we find that the reduced meridional sea surface temperature gradient is a critical factor in the weakening of the East Asian summer monsoon over Northern China and in maintaining drought conditions during the early Pliocene relative to the late Pliocene. Our results expand the Asian inland paleoclimate records during the Pliocene, providing new information on past Asian monsoon precipitation variations during the warm Pliocene with potential implications for future hydroclimate changes under greenhouse warming in East Asia. Key Points: Severe drought conditions persisted throughout the early Pliocene epoch over Northern ChinaWe link these conditions to the reduced meridional temperature gradient in the Pacific OceanThe reduced Pacific meridional temperature gradient acts to weaken the summer East Asian monsoon precipitation over Northern China [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) performances of the recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5, are evaluated to anticipate the expected changes in climate over Egypt. Thirteen GCMs and their multi‐model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5) and two shared socio‐economic pathways (SSP 2–4.5 and 5–8.5). The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce Egypt's spatial distribution and seasonal climate variability. However, the bias in CMIP5 was higher than that for CMIP6. The uncertainties in simulating seasonal variability of rainfall and temperatures were lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (10–26 mm) in the economically crucial northern region than that estimated using CMIP5 (0–17 mm), compared to 133.5 mm rainfall in the base period. CMIP6 also projected a 0.74–1.63°C more rise in Tmax and Tmin compared to CMIP5 by the end of the century. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 models. Therefore, Egypt needs to streamline the existing adaptation and mitigation measures to account for climate projections. [ABSTRACT FROM AUTHOR]

This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization. [ABSTRACT FROM AUTHOR]

The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2 (known as the "Equilibrium Climate Sensitivity," or "ECS") is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either. [ABSTRACT FROM AUTHOR]

We examine the historical evolution and projected changes in the hydrography of the deep basin of the Arctic Ocean in 23 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The comparison between historical simulations and observational climatology shows that the simulated Atlantic Water (AW) layer is too deep and thick in the majority of models, including the multi‐model mean (MMM). Moreover, the halocline is too fresh in the MMM. Overall our findings indicate that there is no obvious improvement in the representation of the Arctic hydrography in CMIP6 compared to CMIP5. The climate change projections reveal that the sub‐Arctic seas are outstanding warming hotspots, causing a strong warming trend in the Arctic AW layer. The MMM temperature increase averaged over the upper 700 m at the end of the 21st century is about 40% and 60% higher in the Arctic Ocean than the global mean in the SSP245 and SSP585 scenarios, respectively. Salinity in the upper few hundred meters is projected to decrease in the Arctic deep basin in the MMM. However, the spread in projected salinity changes is large and the tendency toward stronger halocline in the MMM is not simulated by all the models. The identified biases and projection uncertainties call for a concerted effort for major improvements of coupled climate models. Plain Language Summary: Coupled climate models are crucial tools for understanding and projecting climate change, especially for the Arctic where the climate is changing at unprecedented rates. A cold fresh layer of water (aka halocline) has been protecting sea‐ice at the surface from the warm layer of water (aka Atlantic Water layer) which flows underneath and could potentially accelerate sea ice melting from below. Climate change disturbs this vertical structure by changing the temperature and salinity of the Arctic Ocean (in a process known as Atlantification and Pacification) which may lead to additional sea ice basal melting and accelerate sea ice decline. We examined the simulated temperature and salinity in the Arctic Ocean deep basin in state‐of‐the‐art climate model simulations which provided the basis for the IPCC Assessment Report. We found that although there are persistent inaccuracies in the representation of Arctic temperature and salinity, the Arctic Ocean below 100 m is subject to much stronger warming than the average global ocean. On the other hand, the upper Arctic Ocean salinity is projected to decrease, which on average may strengthen the isolation of sea ice from Atlantic Water heat in the Arctic deep basin area. Key Points: A too deep and thick Arctic Atlantic Water layer continues to be a major issue in contemporary climate models contributing to the CMIP6The Arctic Ocean below the halocline is subject to much stronger warming than the global mean during the 21st centuryThe multi‐model mean upper ocean salinity is projected to decrease in the future but with high uncertainty [ABSTRACT FROM AUTHOR]

This study, for the first time, investigates the physical association between Predecessor Rain Events (PREs) and peak runoff generation in seven catchments over the Upper Mahanadi River basin (UMRB), India. A statistical–dynamical framework is developed to assess the compounding impact of PREs (as preconditioned events) versus riverine floods during both retrospective and projected climate. Based on models' fidelity to capture historical climatology, we select four out of nine Global Climate Models (GCMs) during historical (1980–2005), and the three projected time windows, that is, near future (2010–2039), mid‐century (2040–2069), and the far future (2070–2099) planning horizons with RCP8.5 emission scenario. We assess changes in compound flood hazards in historical versus projected periods using a newly proposed Bivariate Hazard Ratio (BHR) index, which represents the ratio between bivariate return periods during the projected versus the historical time windows. Assessing bivariate return periods (characterized by 'AND' operator) of rainfall‐driven compound floods shows decreased flood hazard in the mid‐century and far‐future planning horizons. Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, overall we show considering PRE as the covariate, floods in larger catchments show an increase in compound flood hazard in the projected period. [ABSTRACT FROM AUTHOR]

Natural climate variability poses a limit to the confidence in regional climate projections, particularly for the near‐term. Therefore, the next decades are of importance for regional impact studies and the development of regional adaptation plans. This article presents results from an ensemble of regional climate model simulations (years 1996–2005 and 2026–2035) that are used to examine the effects of both anthropogenic forcing and natural variability associated with the Pacific Decadal Oscillation (PDO) on near‐term climate projections for the Hawaiian Islands. The Community Earth System Model Large Ensemble (CESM‐LE) is used in conjunction with the Weather Research and Forecasting (WRF) model for downscaling. The climate responses to the PDO and anthropogenic forcing are isolated and analyzed separately. In response to anthropogenic forcing, significant increases in surface air temperature, of ∼0.8 K, are projected at low elevations. Stronger warming, of up to 1.3 K, is seen at higher elevation areas. Future climate simulations show significant increases in wet season rainfall, of ∼10%–20%, along the windward slopes of Big Island and Maui. Rainfall patterns during the positive PDO phase are projected to reverse in sign, leading to drier conditions, by ∼10%–30%, at many locations. Future climate simulations show daily rainfall extremes will increase, by up to ∼10%–15%, at many locations. Daily temperature extremes are also projected to increase significantly, by up to 1.4 K. Overall, results indicate that natural variability will continue to contribute to uncertainty in near‐term rainfall projections for the Hawaiian Islands, masking the forced signal. Plain Language Summary: This project studies how temperature and precipitation will change across the Hawaiian Islands in the near future due to human‐induced causes (e.g., burning coal and other fossil fuels). This is done by comparing climate model simulations for two 10‐year periods (1996–2005 vs. 2026–2035). Further, this research evaluates how natural climate variations affect Hawaiian climate. The main analysis concentrates on an important type of natural climate variability in the North Pacific, the Pacific Decadal Oscillation. This research implements dynamical downscaling, that is, a regional climate model is used to refine global climate model simulations in order to better capture the local weather patterns in Hawai'i. We find that land and ocean temperatures across the Hawaiian Islands region will become warmer due to human‐induced causes. We also find that rainfall will likely increase for some areas of Hawai'i, particularly the mountain slopes, posing an increased risk for flooding. The simulations reveal that natural climate variability will continue to contribute to near‐term climate variations in this region. While the natural climate variability limits our confidence in the projected rainfall changes, it is hoped that our findings will assist decision makers across the State of Hawai'i in creating climate adaptation plans. Key Points: Significant increases in near‐surface temperature and sea surface temperature are projected across the Hawaiian Islands regionSignificant increases in mean and extreme rainfall are projected for some areas of the islandsNatural decadal variability will continue to mask the forced signal in near‐term rainfall projections for the Hawaiian Islands [ABSTRACT FROM AUTHOR]

This study evaluates the precipitation over East Asia simulated by the variable resolution CAM‐MPAS featuring the Model for Prediction Across Scales (MPAS) atmospheric dynamical core coupled with the physics parameterizations of the Community Atmosphere Model (CAM) version 5.4. Two CAM‐MPAS experiments, one with a global quasi‐uniform resolution mesh of 120 km (MPAS‐UR) and the other with a variable resolution mesh of 30–120 km refined over East Asia (MPAS‐VR), are conducted from 1989 to 2005 following the Atmospheric Model Intercomparison Project protocol. Two regional climate model simulations at ∼25 km resolution from the Coordinated Regional Downscaling Experiment East Asia second plan are also analyzed for comparison. Results show that CAM‐MPAS performs better than the selected regional models in simulating the precipitation climatology over East Asia. Compared with MPAS‐UR, MPAS‐VR with refinement over East Asia better simulates the precipitation over and around the Tibetan Plateau (TP), the upper‐level circulation, the precipitation frequency and intensity in various regions of China, and the interannual precipitation pattern associated with the East Asian summer monsoon. Both decreased grid spacing and the use of finer‐scale terrain information contribute to the improvements in MPAS‐VR relative to MPAS‐UR. However, decreased grid spacing is more essential for simulating the precipitation magnitude over TP and the location of peak precipitation south of TP, likely due to the better resolved physical and dynamical processes associated with orographic precipitation. The 30–120 km CAM‐MPAS model is shown to be a promising tool for precipitation simulation over East Asia. Plain Language Summary: Precipitation and associated cloud and radiation processes can affect the energy budgets and water supply over land and thus the livelihoods of billions of people over East Asia. However, traditional low‐resolution global climate models still have difficulty in realistically simulating the mean‐state and variability of precipitation over East Asia, partly due to their insufficient resolution to resolve the complex terrain and associated synoptic‐scale systems. In this work, we perform model experiments using the Community Atmosphere Model coupled with the atmospheric dynamical core from the Model for Prediction Across Scales with a variable resolution mesh to assess the impact of regional refinement on the precipitation simulation over East Asia. Experiment with higher resolution (30 vs. 120 km) over East Asia better simulates the total precipitation amount over and around the Tibetan Plateau (TP), as well as precipitation frequency, intensity, and interannual variability associated with the East Asian summer monsoon. The finer grid spacing is found to contribute more to the improvements in areas adjacent to TP than the use of finer terrain information, likely due to better resolved physical and dynamical processes associated with orographic precipitation. Key Points: The precipitation climatology over East Asia can be reasonably reproduced by a global climate model CAM‐MPASRegional refinement over East Asia improves the precipitation simulation over East Asia in various aspectsFiner grid spacing is more essential for precipitation simulation around large‐scale terrain than using finer‐scale terrain information [ABSTRACT FROM AUTHOR]