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Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

Both long‐term but small number of national stations and short‐term but large number of regional stations have been frequently used to study the extreme hourly precipitation (EXHP) in China. However, few studies focus on the differences of the two for revealing the features of EXHP. In this study, the characteristics of EXHP in Zhejiang Province are investigated using three rainfall data sets at three threshold criteria. The comparison between different data sets shows that increasing the station density can better reflect the climatic spatial distribution of EXHP thresholds if long‐term data is absent. The majority of EXHP can be classified into four weather types: the southwesterly wind type (30.7%–48.5%), the trough type (12.2%–23.6%), the tropical cyclone (TC) type (11.4%–17.5%) and the easterly wind type (4.9%–17.9%). The selection of stations is more sensitive to the proportions of the four weather types than the statistical years and threshold criteria. The monthly and diurnal variations of EXHP, as well as their differences revealed by the three data sets, are varied by weather type. Only using national stations cannot distinguish the spatial differences between the TC type and the easterly wind type, and there is an underestimation for southwesterly wind type and trough type in the mountainous area of southwest Zhejiang. The statistical year and station height are the main reasons for the differences in the duration of EXHP events calculated by different data sets, with the TC type having the largest effect and the southwesterly wind type the smallest. Plain Language Summary: Station data is the most common and reliable data for studying the characteristics of precipitation in China. There are two sources of station data available, that is, national station data with long time series but sparse and regional stations with short time series but numerous. However, few studies have compared the differences between these two sources of data for revealing the characteristics of extreme hourly precipitation (EXHP). In this study, the possible impact of different data sets for revealing the characteristics of EXHP under four weather types are investigated in Zhejiang Province. Zhejiang is located on the southeast coast of China with a long coastline and many mountains. Results suggest that increasing the station density can better reflect the spatial distribution of the EXHP thresholds if long‐term data is absent. EXHP events with strong synoptic‐scale forcing are more sensitive to the results obtained by different data sets. As the national stations are mostly located in the plains, the high frequency centers of EXHP are biased in each weather type if only using national stations, especially in southwest Zhejiang and near the coast. In summary, this paper provides additional perspectives for understanding the characteristics of regional EXHP by using the most common observations. Key Points: The selection of stations is sensitive to the proportions of the four main weather types in Zhejiang where extreme hourly rainfall occursExtreme hourly precipitation events with strong synoptic‐scale forcing are more sensitive to results obtained using different data setsThe sparse national stations located on the plains could hardly reflect the spatial distribution of extreme rainfall in mountainous Zhejiang [ABSTRACT FROM AUTHOR]

An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremesMagnitude of observed blackouts exceeds climatologically expected valuesUnexpectedly, longer CMLs experience more blackouts [ABSTRACT FROM AUTHOR]

Temperature increases are associated with an intensification of heavy sub‐daily extreme rainfall by approximately 7% per °C, in accordance with the Clausius‐Clapeyron (CC) relation. As a result of this intensification, there are concerns regarding the increased frequency and magnitude of floods in small to medium‐sized catchments. The high‐resolution two‐dimensional weather generator (WG), AWE‐GEN‐2d, offers an ideal tool to simulate the climate variables required to assess the catchment‐scale hydrological response at high resolution. However, it lacked an explicit representation of the relationship between temperature and precipitation that can mimic the CC relationship. Therefore, we introduce a newly revised version of the model, named AWE‐GEN‐2d‐CC, designed to mirror the observed CC scaling by conditioning the simulation of precipitation properties (intensity and area) on temperature. We demonstrate the model's efficacy in representing future extreme rainfall by simulating their potential impact on the hydrological response of a mountainous catchment in the Swiss Alps. Based on observations and future climate model projections, AWE‐GEN‐2d‐CC was used to generate large ensembles of present and end‐of‐century climate data at hourly and 2‐km resolutions, used subsequently as input to Topkapi‐ETH, a physically‐based, distributed hydrological model. The new version of the WG successfully mimics the CC scaling of heavy rainfall, leading to an intensification of short‐duration heavy rainfall in future climates, in contrast with the results obtained using the original model. This allows for a more realistic assessment of future rainfall impact on hydrological response, which, in the demonstration application, shows a modulated effect even for short durations. Plain Language Summary: As global temperatures continue to rise, so does the intensity of heavy rainfall, which can lead to increased flooding in small to medium‐sized areas. The challenge is predicting not only how heavy rainfall will intensify at a specific point, but also how it is distributed over a catchment area. The AWE‐GEN‐2d model proved to be an effective tool in this respect. We introduce a new version of that model, called AWE‐GEN‐2d‐CC, which explicitly considers the relationship between temperature and rainfall. We tested our model on a mountainous area in the Swiss Alps, using past observations and future climate model projections, and found that the model can successfully predict heavy rainfall intensification in the future, especially for short, intense bursts of rain. This improves the plausibility of the results compared to those obtained with the older version of the model. Despite the predicted increase in heavy rainfall, we found the effect on river flows in the catchment was less drastic. Our study highlights the importance of accurately modeling the impact of temperature on rainfall. In areas prone to high‐intensity storms, our model can assist stakeholders in preparing for future weather extremes. Key Points: A new version of the AWE‐GEN‐2d weather generator model is presentedThe new model explicitly simulates the temperature dependency of extreme precipitation, enabling a realistic simulation of future extremesA climate change hydrological impact study over an alpine catchment is presented [ABSTRACT FROM AUTHOR]

Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater. Key Points: Groundwater potential mapping was carried out at the Central Highlands of VietnamHybrid Machine learning Models based on Naïve Bayes Tree were developed and usedResults showed that the proposal models are potential and accurate tools [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations. Plain Language Summary: Numerical weather prediction models depend on certain empirical formulations known as parameterizations to estimate precipitation. However, these methods often fall short due to the intricate dynamics of rainfall, which involves numerous small‐scale interactions that these models are unable to fully capture. To counteract these limitations, our study deploys a form of machine learning known as deep learning (DL) to predict precipitation. This DL model utilizes fundamental weather variables derived from NWP models to make its estimations, serving as a remedy for the inherent weaknesses of traditional models caused by the uncertainties in their parameterization schemes. The implementation of our DL model resulted in a significant enhancement in rainfall prediction accuracy, particularly in the case of extreme precipitation events. This suggests that the application of machine learning strategies could be a promising approach to improve the reliability of rainfall forecasts, a crucial element for effective weather prediction and water resource management. Key Points: We employ a transformer based deep learning model to improve the accuracy of precipitation estimation in numerical weather prediction modelsVarious training strategies were implemented to manage the highly skewed precipitation data leading to improvements in heavy rainfall eventsEvaluation conducted with multiple metrics including skill score, quantile and spatial distribution as well as two case studies [ABSTRACT FROM AUTHOR]

Impacted by both the large‐scale climate change and the local hydrometeorological atmosphere, the spatiotemporal structure and properties of short‐duration extreme rainfall events in urban areas remain a complex issue. This study explores the spatiotemporal pattern variability of short‐duration rainstorm events using an exceptional sub‐hourly gauge network in Shanghai, China. A catalog of 207 "independent" rainstorms is extracted. The hierarchical clustering method is used to examine the spatial and temporal features including rainfall duration, intensity, and coverage for each district with different degrees of urbanization in the 2014–2018 period. The results point to a pronounced spatial heterogeneity with heavier rainfall magnitude in the north. In the urban core area, intensive short‐duration rainstorms hit more frequently than in other districts. Rainstorms can be divided into three types from the spatiotemporal process: Type delayed‐slow, Type advanced‐fast, and Type advanced‐slow. The delayed‐peak with a higher slope occurs more in the west and the advanced‐peak more along the coastal line. The other type of advanced‐peak storm with a reverse trend of intensity and coverage concentrates in June–August of the year and the 16:00–20:00 of the day. While 65.6% of the delayed‐peak storms only occur in one district, more than half of the advanced‐peak appear without other types at the same time. These findings show the importance of observation‐based analysis in understanding the spatiotemporal pattern of short‐duration storms and provide a reference for storm design of sub‐regions in urban areas. Key Points: There is a pronounced spatial heterogeneity of short‐duration rainstorms in Shanghai cityRainstorms can be divided into three types from the spatiotemporal processThe occurrence of different rainstorm types varies seasonally and diurnally [ABSTRACT FROM AUTHOR]

The WIVERN (WInd VElocity Radar Nephoscope) mission, currently under the Phase‐0 of the ESA Earth Explorer program, promises to provide new insight in the coupling between winds and microphysics by globally observing, for the first time, vertical profiles of horizontal winds in cloudy areas. The objective of this work is to explore the potential of the WIVERN conically scanning Doppler 94 GHz radar for filling the wind observation gap inside tropical cyclones (TCs). To this aim, realistic WIVERN notional observations of TCs are produced by combining the CloudSat 94 GHz radar reflectivity observations from 2007 to 2009 with ECMWF co‐located winds. Despite the short wavelength of the radar (3 mm), which causes strong attenuation in presence of large amount of liquid hydrometeors, the system can profile most of the TCs, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. The statistical analysis of the results shows that, (a) because of its lower sensitivity, a nadir pointing WIVERN would detect 75% of the clouds observed by CloudSat (45% of winds with 3 m s−1 accuracy, in comparison to CloudSat sampling of clouds), (b) but thanks to its scanning capability, WIVERN would actually provide 53 times more observations of clouds than CloudSat in TCs (30 times more observations of horizontal winds), (c) this corresponds to about 350 (200) million observations of clouds (accurate winds) every year. Such observations could be used to shed light on the physical processes underpinning the evolution of TCs and in data assimilation in order to improve numerical weather prediction. Plain Language Summary: Despite their huge impact on tropical weather, tropical cyclones are still not fully understood with uncertainties in the predictions of their evolution and trajectory. In order to provide more detailed observations of tropical cyclones, a ground‐breaking spaceborne observing system hosting a conically scanning Doppler high frequency radar that could provide information both on in‐cloud winds and cloud mass contents is proposed. Despite the fact that the observations from such radar are strongly attenuated in presence of heavy rain, the system can sample most of the tropical cyclones, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. Our notional study, based on the climatology of tropical cyclones collected during 3 years by a spaceborne nadir pointing non‐Doppler radar, demonstrates that the newly proposed satellite would provide hundred millions observations of clouds and accurate winds in tropical cyclones every year. Such observations could be used to improve our understanding of the processes underpinning the evolution of tropical cyclones and hence, their forecast. Key Points: Attenuation makes a spaceborne 94 GHz radar blind for only 5% of the cloudy/precipitating part of tropical cyclones (TCs)Thanks to its scanning capability, WIVERN would provide about 50 times more observations in TCs than the CloudSat sampling of cloudsWIVERN wind uncertainty is dominated by pulse‐pair estimator noise (3 m/s at 1 km horizontal resolution and reflectivity exceeding −15 dBZ) [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement Mission (GPM) provides two different sources of post‐real‐time satellite‐based rainfall estimates, including the Integrated Multi‐satellitE Retrievals for GPM Final Run (herein IMERG‐F) and Global Satellite Mapping of Precipitation‐Gauge (herein GSMaP‐G). However, relative to IMERG‐F, GSMaP‐G has been less thoroughly evaluated in the context of studying rainfall variations over Luzon, Philippines. Using rain‐gauge observations over Luzon as a reference base, this study aimed to clarify if GSMaP‐G v07 is more capable than IMERG‐F v06 in regard to representing rainfall variations over Luzon at multiple timescales (including intraseasonal, annual, and interannual). The results revealed that both IMERG‐F and GSMaP‐G performed better in regard to depicting the temporal phase evolution for the wet season rainfall than they did for the dry season rainfall for Luzon. However, relative to IMERG‐F, GSMaP‐G exhibited better performance in most examined features, including (a) the detection of temporal variations (both phase and amplitude) of rainfall on the intraseasonal, annual, and interannual timescales; (b) the representation of spatial differences with more rainfall occurring from April to September for western Luzon and from October to March for eastern Luzon; (c) the detection of occurrence of rainfall events at various intensities. The analysis also demonstrates that the difference between the performance of IMERG‐F and GSMaP‐G in regard to quantitative rainfall estimation is larger during the wet season than it is during the dry season. The possible cause for their performance differences over Luzon is the differences in the gauge‐adjustment method and the biases in its pre‐non‐gauge‐adjusted product. Key Points: The ability of Global Satellite Mapping of Precipitation‐Gauge (GSMaP‐G) v07 and Integrated Multi‐satellitE Retrievals for GPM Final Run v06 to depict Luzon rainfall features was evaluatedGSMaP‐G is better for most annual, interannual, and intraseasonal features and rainfall contrast between eastern and western LuzonThe performance difference is caused by the difference in the gauge adjustment method and the biases in its pre‐non‐gauge‐adjusted product [ABSTRACT FROM AUTHOR]

This study analyzes the interannual variability of boreal springtime (March and April) diurnal rainfall (hereafter SDR) over the second biggest Island of the Indian Ocean, Sri Lanka (SL), and expresses the possible effects of the El Niño Southern Oscillation (ENSO) events in modulating this variability. The analysis is primarily based on high spatiotemporal resolution satellite precipitation estimates and reanalysis data from 2001 to 2019. Results indicate that the SDR in SL exhibited a consistent afternoon peak throughout the study period. In contrast, the SDR's amplitude consists of a notable 2 to 4‐year oscillation period, similar to the oscillation period of ENSO events. Further analysis revealed that the interannual variation of the SDR's amplitude in SL has a negative relationship with the springtime and previous winter sea surface temperature changes in the central and eastern tropical Pacific Ocean covering NINO3.4 and NINO3 regions (referred to as ENSO indices). When there is a winter La Niña (El Niño), the following SDR in SL is more (less) active. A possible explanation is that during La Niña (El Niño) years, the region around SL experienced an enhanced (suppressed) cyclonic circulation, enhanced (suppressed) large‐scale moisture flux convergence, and enhanced (suppressed) local‐scale diurnal varying moisture flux convergence, leading to an increase (decrease) in the potential for rainfall and more (less) active SDR over SL. These findings highlight the potential to use ENSO indices to predict the interannual variation of SDR activities over SL. Plain Language Summary: This study looked at the yearly changes in springtime rainfall that occurs during the day in Sri Lanka (called "spring diurnal rainfall" or SDR). The study used satellite precipitation estimates and global reanalysis data from 2001 to 2019. The results showed that SDR in Sri Lanka usually happens in the afternoon and that the amount of SDR can change from year to year, with a pattern consisting of 2 to 4‐year oscillation periods. This oscillation period is similar to the oscillation period of El Niño Southern Oscillation (ENSO). The study also found that when El Niño occurs in the Pacific Ocean, its related circulation pattern changes tend to reduce the moisture supply in the region around Sri Lanka, leading to a decrease in the amount of SDR in Sri Lanka. In contrast, the opposite pattern happens when there is a La Niña in the Pacific Ocean, increasing the SDR in Sri Lanka. The ENSO patterns in the Pacific can be measured by the sea surface temperature changes, and this information in winter can be used as an indicator to predict the increase or decrease in following SDR in Sri Lanka. Key Points: Sri Lanka's spring diurnal rainfall (SDR) has an interannual variability in magnitude change with a 2 to 4‐year oscillationDuring La Niña (El Niño), regional moisture supply increases (decreases), resulting in a larger (smaller) SDR amplitude in Sri LankaEl Niño Southern Oscillation indices may be potentially used to predict SDR interannual variation in Sri Lanka [ABSTRACT FROM AUTHOR]

Precipitation products with high resolution for urban areas remain an important and challenging issue. The main purposes of this study are to evaluate the accuracy of the China Meteorological Forcing Data Set (CMFD) in the Shanghai megacity and to improve the accuracy of the CMFD precipitation. With the rain gauge observations for the 2013–2018 period, we evaluate the error and detection ability of CMFD at the sub‐daily, daily, and monthly scales, and examine the spatial variation of CMFD performance over the region. The Geographic Differential Analysis (GDA) method is used to calibrate the CMFD precipitation data set. The results show that the performance of CMFD precipitation varies from sub‐daily to monthly time scales with better accuracy at the monthly scale in the study region. The CMFD can capture the spatial variability over the region with less error in the north and outliers in the center. The GDA‐based calibration framework can significantly improve the capability of CMFD products, especially at the sub‐daily and daily scales. The calibrated CMFD can provide reliable precipitation estimates and capture more extreme precipitation events. The improved CMFD products can provide a key basis for applications related to hydrological and meteorological hazard monitoring and assessment in the Shanghai area. The overall framework is scalable and could be readily generalized to other regions, and the calibrated product can be used in the research of urban natural disaster risks and other studies. Key Points: The performance of Meteorological Forcing Data Set (CMFD) precipitation varies from 3‐hourly to monthly time scales with the best accuracy at a monthly scaleThe CMFD can capture the rainfall spatial variability over Shanghai cityThe Geographic Differential Analysis‐based calibration method can significantly improve the performance of CMFD products [ABSTRACT FROM AUTHOR]

Model output from climate projections often requires bias‐adjustment to compensate for systematic model errors. A bias‐adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity‐duration‐frequency (IDF) modeling. A validation is performed within a pseudo‐reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO‐CORDEX ensemble over Belgium. The scaling‐based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above‐mentioned scale‐invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias‐adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations. Plain Language Summary: Heavy precipitation is known to have major impacts on society, including crop damage, soil erosion, landslides, and increased flood risk. In recent years, there is a growing body of evidence that human‐induced climate change has intensified extreme precipitation on the global scale. Both regional and global climate models predict that this will continue as the climate warms. Nevertheless, trend analysis or climate‐change attribution of local rainfall extremes remains particularly difficult for many reasons including high variability in space and time. Therefore it is essential to use statistical methods that make use of all available information. Climate model simulations are known to exhibit systematic biases. Here, for the first time, we use a well‐established technique in hydrology (intensity‐duration‐frequency modeling) to adjust extreme precipitation intensity as a function of the rainfall duration and event probability. Validation results with a large multi‐model ensemble illustrate that the new method improves upon existing approaches. As an illustration, we present a bias‐adjusted multi‐model climate ensemble for future projections of heavy rainfall statistics over Belgium. Key Points: New bias‐adjustment methods for subdaily extreme precipitation consistent across different precipitation durationsPseudo‐reality cross‐validation shows higher skill than existing bias‐adjustment methods for subdaily extremesApplication to regional climate model projections over Belgium shows, despite large uncertainties, consistent future increase in subdaily precipitation extremes [ABSTRACT FROM AUTHOR]

Debris flow runout poses a hazard to life and infrastructure. The expansion of human population into mountainous areas and onto alluvial fans increases the need to predict and mitigate debris flow runout hazards. Debris flows on unconfined alluvial fans can exhibit spontaneous self‐channelization through levee formation that reduces lateral spreading and extends runout distances compared to unchannelized flows. Here we modify the D‐Claw shallow flow model in two ways that are hypothesized to generate levees. We evaluate these modifications with observations from a large‐scale flume experiment. We investigate model performance when including the effect of two different friction sub‐models, as well as the inclusion of segregation effects on granular permeability. Results show that, for a wide range of plausible model input parameters, simulations including the effects of segregation promoted modeled levee formation, whereas simulations without the effects of segregation did not create levees. Further, using a forward predictive framework, simulations with the effects of segregation were more likely to better model the magnitude of debris flow depth and runout distance, whereas simulation timing of the debris flow was affected by the choice of friction sub‐model. Our results indicate that including the effects of segregation on granular permeability can improve the likelihood of better predictions of debris flow depth and runout prior to an event occurring. Plain Language Summary: Hillsides that receive heavy rainfall sometimes experience a debris flow, a flood‐like mix of water, soil, and rocks. As more people move into mountainous regions, the hazard that debris flows present increases. In this study, we use a computer simulation to model debris flow behavior. We look at different ways to model parts of the physics, specifically, friction and the effects of the debris material size, on the area that debris flow reaches. We find that capturing the effect of different sized debris is important to recreate the self‐forming debris flow channel that can extend the length a debris flow can travel. Including these effects improves the predictive capability of the computer simulations. Improving our understanding and ability to model debris flow runout is important to predicting locations where a potential hazard exists from debris flows. Key Points: Modeling different material properties through the effects of segregation in D‐Claw allows for spontaneous self‐channelizing levee formationIncluding the effects of segregation on granular permeability improves debris flow simulation performance for recreating a large scale experimental debris flow [ABSTRACT FROM AUTHOR]

Rise in mean temperature put a great deal of uncertainty about how weather and climate extremes may play out, particularly in India's varied climatic zones. Consequently, it is important to understand the possible changes in both magnitude and direction of weather and climate extremes like rainfall for different warming levels of 1.5 and 2°C scenarios concerning preindustrial and present levels. Hence in the present study, the precipitation behavior of seven North Indian states that is, Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand carefully studied using CMIP5 models. Future projections of precipitation has been done for the Paris Agreement global warming level of 1.5 and 2°C scenarios. Along with model validation and future projections of precipitation, the return period of extreme rainfall is also discussed to understand the behavior of the occurrence of extreme precipitation. Statistical analysis shows that the ensemble means have the least error as compared to the other six CMIP5 models. Therefore, future analysis has been done with the ensemble mean. Our findings show that the precipitation is likely to decrease in the 1.5°C scenarios, while it is likely to increase in the 2°C scenarios. The occurrence and intensity of extreme rainfall events are likely to be more frequent in all the models. The return period of the extreme rainfall events is likely to increase in all the states in both the warming scenarios. A three‐fold rise is likely to increase extreme rainfall events in the 2°C scenario. Key Points: Study performs a detailed analysis of rainfall behavior of the northern states India during preindustrial, present, 1.5 and 2° scenariosSix climate models were selected out of 26 CMIP5 climate models based on the capability of representing the monsoon rainfall over IndiaIn the 2°C scenario, the recurrence period for intense rainfall events will likely to increase by a factor of 3 [ABSTRACT FROM AUTHOR]

The basic and derived meteorological parameters, are important factors that affect the epochal changes of monsoonal drought/flood events which consequently influence the socio‐economic aspects of India. The anomalous tricade departure of parameters namely rainfall, surface pressure, surface temperature, zonal and meridional wind, cloud cover (CC), outgoing longwave radiation, latent heat flux (LHF), sensible heat flux, net incoming shortwave radiation flux and net heat flux (NHF) have been considered between past, and recent tricade respectively drought and flood events. An anomalous decrease (−3 to −2 mm/d) in rainfall over the southern Arabian sea (AS), southern peninsular India and Bay of Bengal during past and recent tricade, on the other side increase (2–2.5 mm/d) in rainfall over the equatorial Indian ocean (EIO) during drought. The drought episode of the recent tricade is showing basin wise significant increase in LHF (20–30 W/m2) and decrease in NHF (−30 to −60 W/m2) from the Somali coast to Indonesian region which is linked with changing oceanic processes linked with the Indian ocean warming. The comparison of past and recent monsoonal flood has shown decrease in the CC over EIO and AS region which supported anomalous highest departure in LHF (12 W/m2) over EIO. A direct association of dry and moist static energy variations have been found to be linked with the surface fluxes during Indian summer monsoon season. Further, epochal differences of vertically integrated moisture flux convergence is consistent with flood/drought rainfall anomaly. Thus, radiative imbalance also plays a secondary role in determining epochal nature of drought/flood variability and its nature. Plain Language Summary: The epochal variation of basic meteorological parameters and surface energy fluxes in monsoonal drought and flood episodes have been analyzed from 1961 to 2020. In this study, over the southern Arabian Sea (AS), southern peninsular India, and the Bay of Bengal decreasing rainfall during past tricadal monsoonal drought events while an increase in rainfall over the Indian Ocean (IO) has been observed by a change in the amount of cloud cover and surface energy fluxes. The undulating behavior of convective heating is associated with a significantly changing pattern of surface heat fluxes observed in past and recent monsoonal drought and flood episodes. Determining the epochal nature of drought and flood variability and its nature. The epochal departure in net incoming shortwave radiation flux is a considerable factor in contrasting dynamical features of drought and flood events along with monsoon circulation over the Indian region. Key Points: The convective heating is a major role in changing surface fluxes during drought and flood eventsThe radiative imbalance plays a secondary role in determining drought and flood variability and its natureThe elevated value of net incoming shortwave radiation would cause more severe drought than flood in near future [ABSTRACT FROM AUTHOR]

We investigated the resolution sensitivity of the Global‐to‐Regional Integrated forecast SysTem global nonhydrostatic model characterized by explicit dynamics–microphysics coupling using varying uniform resolutions (120, 60, 30, 15, and 5 km). The experiments followed the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) winter protocol, which covers a 40‐day integration. These simulations did not activate parameterized convection. One 120 km test with parameterized convection was performed as a coarse‐resolution reference. Other model configurations for different simulations were kept as consistent as possible. Our results showed that the model gradually improved its representation of the fine‐scale features as the resolution increased. The 5 km simulation was overall close to a 3.75 km simulation during the first 12 days of the DYAMOND winter. With respect to the mean climate, the 5 km simulation had a more realistic rainfall distribution than the lower resolution explicit convection simulations. Cloud water and the related physical fields (e.g., shortwave cloud radiative forcing) had a large resolution sensitivity. The tropical rainfall frequency–intensity spectra became more realistic in the 5 km explicit convection simulation, but the 120 km run with parameterized convection showed a more realistic mean climate. As the resolution increases, the mean bulk effect of finely resolved model convection gradually converges to that of parameterized convection. The mean climate of this storm‐resolving model has slightly higher rainfall biases than a parameterized convection coarse‐resolution model, highlighting the importance of balancing resolved‐ and under‐resolved model convection for developing a unified multiscale global model. Key Points: The resolution sensitivity of the Global‐to‐Regional Integrated forecast SysTem nonhydrostatic model with an explicit‐convection setup is assessed from O (100 km) to O (km) scalesThe model gradually improves its representation of fine‐scale fluid structures with increasing resolutionThe interaction between cumulus ensemble and large‐scale flow of a Global storm‐resolving model overall converges to that of a parameterized convection model [ABSTRACT FROM AUTHOR]

Extreme torrential rainfall events are low‐probability events. A "China‐Record Extremely Heavy Rainfall" (CREHR) event with rain rate of 201.9 mm/hr occurred on 20 July in Zhengzhou in North China. Using high‐density meteorological observations, ERA5 reanalysis data, remote sensing data from China's FY‐4A satellite, and numerical simulation, we revealed that sufficient warm–humid airflows were continuously transported to the Zhengzhou area via multiple pathways that were mainly modulated by the large‐scale western Pacific subtropical high to the north along with the anomalies of the westerly belt and a meso‐scale binary typhoon system. At the local scale, under the combined action of the vertical circulation that caused low‐level convergence and high‐level divergence, and the rapid uplift of water vapor related to the blocking by mountainous terrain, this CREHR event was eventually triggered. Particularly, our present work shows the roles of both typhoon Cempaka and meso‐scale convective systems to south of Henan province can not be ignored, producing a strong super‐thick water vapor transport layer. The existence of the binary typhoon raises the level of uncertainty involved in the water vapor transport of this CREHR event. Plain Language Summary: Extreme torrential rainfall events are low‐probability events. The rainfall from 16:00 to 17:00 Beijing time on 20 July in Zhengzhou in North China reached 201.9 mm, which was a record in mainland China—a so‐called "China‐Record Extremely Heavy Rainfall" (CREHR) event. By using high‐density meteorological observations, ERA5 reanalysis data, remote sensing data from China's FY‐4A satellite, and numerical simulation, we found that sufficient warm–humid airflows were continuously transported to the Zhengzhou area via multiple pathways that were mainly modulated by the large‐scale western Pacific subtropical high to the north along with the anomalies of the westerly belt and a meso‐scale binary typhoon system. At the local scale, under the combined action of the vertical circulation that caused low‐level convergence and high‐level divergence, and the rapid uplift of water vapor related to the blocking by mountainous terrain, this CREHR event was eventually triggered over northern mainland China. Our findings reveal that the existence of the binary typhoon circulation raises the level of uncertainty involved in the water vapor transport of this CREHR event, which has implications for improving our ability to predict and provide early warnings for severe rainstorms and related disasters in cities with such a complex context. Key Points: Sufficient warm‐humid airflows were continuously transported to the Zhengzhou area via multiple water vapor pathwaysMultiple pathways were mainly modulated by the large‐scale western Pacific subtropical high, the westerly belt and mesoscale binary typhoonsNorthward movement of multiple meso‐scale convective systems caused a strong super‐thick water vapor transport layer [ABSTRACT FROM AUTHOR]

Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground‐based radar because these instruments have an uneven distribution in remote areas. Satellite precipitation data sets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. However, the speed and direction of the wind has the interaction with terrain, which leads the uncertainty of the SPDs. This study performed sub‐daily assessments of near‐real‐time and high resolution SPDs (i.e., IMERG, GSMaP, and PERSIANN data sets) during five typhoon‐related heavy precipitation events in the Philippines, with the analysis under the impact due to wind and terrain effect. The aforementioned assessments were performed through a point‐to‐grid comparison by using continuous and volumetric statistical validation indices for the 34‐knot wind radii of the typhoons, rainfall intensity, terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall. The GSMaP data set overestimated the gauge observations during peak rainfall, while the IMERG and PERSIANN data sets considerably underestimated rainfall. The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds. Key Points: Satellite precipitation data sets (SPDs) were evaluated during typhoon‐related heavy precipitation events in the Philippines for the first timeThe high spatial resolution SPDs (0.04° and 0.1°) were assessed in this studyThe performance of SPDs varies clearly with terrain complexity [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are routinely applied for regional climate assessments. The RCM simulated rainfall typically overpredict the light rain/drizzle events. To correct the typical errors noted in RCM rainfall simulations, in this study, three bias‐correction methods: linear scaling (SCL), local intensity scaling (LOCI), and empirical quantile mapping (EQM), have been employed. These methods are used to correct monsoon rainfall simulations from 7 RCMs across 14 agroclimatic zones (ACZs) in India from 1970 to 2005. The corrected rainfall data were compared to the observations obtained from India Meteorological Department. The performance of the three methods was assessed using: probability distribution function, consecutive dry day index (CDD), R95 (rainfall distribution at 95th percentile), and spatial correlation. The results vary spatially across the different ACZs. Overall, SCL method is more effective followed by EQM while LOCI was relatively less effective in correcting the errors. Spatial analysis of the rainfall indicates notable improvements over the Western Himalayan Region, which has a complex topography and land use. Error metricsreveal broad improvements across different ACZs, except for Central Plateau Hill Region, East Coast Plain Hill Region, and Southern Plateau Hill Region. The SCL and EQM perform well, and the results are particularly good for simulating wet days (R95), while no distinct variation was found among correction methods to reduce dry bias (CDD). The results provide ACZ and region‐specific utilization of an effective bias correction technique for impact assessment studies in the Indian monsoon region. Key Points: Linear scaling (SCL) and empirical quantile mapping (EQM) performed well, particularly for simulating wet days (R95)Local intensity scaling (LOCI) method was relatively less effective in correcting the errorsNo distinct variation noted among correction methods to reproduce dry bias (CDD) [ABSTRACT FROM AUTHOR]

Accurate prediction of tropical cyclone (TC) induced rainfall is of great importance to disaster prevention but quite challenging to operational forecasters. This study quantifies the spatial characteristics of the hourly rainfall caused by TCs at several individual weather stations over the south China coast. We use the historical TC datasets from 2004 to 2018 and the hourly rainfall data from six weather stations to investigate the stations' rainfall distribution characteristics induced by TCs within a distance of 700 km to the weather stations. The hourly rainfall data during the study period from six coastal stations: Zhuzilin (ZZL), Shekou Ferry Terminal (SFT), and Wutong Village (WTV) in Shenzhen, and Hong Kong Observatory (HKO), Sha Tin (SHA), and Tai Mo Shan (TMS) in Hong Kong are used to investigate the statistical relationship. Results show that the most hazardous areas for TC‐induced rainfall are TCs within a distance of 200 km relative to the stations. High intensity TCs in the southwest quadrant may bring torrential rainfall to stations within 500 km distance, and low intensity TCs are likely to induce torrential rainfall to stations within 300 km distance when they are in the northeast and northwest quadrants. If TCs are over the region with the azimuth of 60–150° and distance out of 200 km to the six stations, there is almost no rainfall influence on those stations. The findings of this study are valuable to provide information for the local operational forecasters when predicting the potential rainfall along the south China coast due to future TCs. Key Points: The most hazardous areas for tropical cyclones (TCs) induced rainfall are TCs within a distance of 200 km to the stationTCs are more likely to bring torrential rainfall to a coastal station when they are in the southwest quadrant of the stationTCs have almost no impact on rainfall when the azimuth is between 60 and 150° and distance is out of 200 km from the station [ABSTRACT FROM AUTHOR]