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One of the largest uncertainties in future climate projections is the interplay between internally generated and externally forced changes. This study investigates the changes in the link between the North Atlantic Oscillation (NAO) and Mediterranean winter extreme rainfall and dry days by the end of the 21st century compared to present day. We compare two different future pathways and estimate the extent to which the NAO imprint is affected by the global warming level using the latest EC-Earth3 large ensemble historical and future experiments. It is shown that the expected range of winter extremes changes due to internal and unpredictable fluctuations of the NAO largely overcomes the signal associated with externally-forced NAO variations. The NAO is found to exert a similar control on European climate variability, regardless of the amount of warming. For most of the Mediterranean region, magnitude and even sign of projected changes in the NAO-congruent precipitation indices vary substantially across the individual ensemble members according to the corresponding evolution of the NAO. Internal variability provides an average basin-wide contribution of up to 90% or more to the total NAO-driven variability in SSP1–1.9, and of about 80% in SSP5–8.5. Sub-regionally, the anthropogenic component of the NAO link is more evident over the Iberian Peninsula and parts of the central Mediterranean. This emphasises the role of internal variability and related uncertainty in determining the future impact of the NAO via the large spread in the circulation responses. However, the NAO is found to exert a weaker influence on the extreme precipitation total variability in both future scenarios given their future marked increase in total intensity and variance as opposed to the negligible NAO-related trends. Opposite conclusions are drawn for dry days, which are projected to decrease in the future, especially in the northern Mediterranean. Thus, this study also highlights how the variability of future extreme precipitation intensity in the Mediterranean basin will be less dependent on the principal mode of internal climate variability, posing further challenges for prediction and adaptation to weather-related hazards. • The NAO signature does not vary depending on the amount of 2071–2100 warming. • Internal variability of the NAO dominates over the externally-generated NAO changes. • The NAO will exert a weaker influence on extremes total variability in the future. [ABSTRACT FROM AUTHOR]

Compound precipitation and wind speed extremes have the potential to cause significant socio-economic losses. Therefore, this study aims to investigate the association of compound precipitation and wind speed extremes in observed period (1981–2020) and future periods (2021–2060 (F1) and 2061–2100 (F2)) over India. In this study, we have used IMD precipitation and ERA5 windspeed gridded (0.250 × 0.250) data for the observed period. For future analysis, we have used CMIP6-based EC-Earth3 GCM (SSP2–4.5 and SSP5–8.5) and CORDEX CMIP5 RCM (RCP-4.5 and RCP-8.5) climate models for precipitation and wind speed outputs. We have analyzed the characteristics of precipitation and wind speed extremes using three approaches: empirical method, tail dependence analysis, and event coincidence analysis. The empirical analysis revealed that the proportion of the area with >240 Compound Precipitation and Wind speed Extremes (CPWE) on the same day is 2.8% in observed period. However, it increased to 22.22% in future period F2, particularly in the SSP5–8.5 scenario of the CMIP6 case, and this increase is observed in regions like Am, eastern parts of Aw and Cwa, Bwk, and Dsb. The tail dependence analysis revealed that the area with tail dependence >0.5 constitutes approximately 0.72% in the observed period across the study area. However, under climate change scenarios, this percentage varies between 2.45% to 3.66% and notably, the increase is observed in regions such as Am, Bwk, and Dsb. The results of precursor coincidence rate (pcr) shows a high value of 3.52% in the entire study area for the observed period. The future analysis reveals an increase of areas in between 3.18% to 5.14% for different climate change scenarios. Consequently, the trigger coincidence rate (tcr) results also revealed the occurrence of high tcr in the range of 0.52% to 5.79% across the study area within the temporal window. However, the tcr range increases to 2.17% to 23.32% during the monsoon season during the observed period with respect to the days in the temporal window. The overall results showed that the high values of tail dependence, pcr and tcr are concentrated in the Am, coastal areas of Aw and Cwa, as well as in the Bwk and Dsb regions. This will lead to increase the extreme precipitation driven by extreme wind speed in those regions consequently, the risk of flash floods, landslides and natural hazards may rise in the future. Overall, this study provides valuable insights into the spatial distribution of compound precipitation and wind speed extremes across the study area. [Display omitted] • According to CMIP6 projections, the CPWE is rising in India, especially in the Am, BWk, Cwb, and Dsb regions. • Coastal regions, especially the west coast, exhibit higher tail dependence, influenced by features like the Western Ghats. • High pcr and tcr in the Am region, during monsoon season, highlight an increased risk of compound extreme events. [ABSTRACT FROM AUTHOR]

Knowledge of long‐term changes in climate extremes is vital to better understand climate variability and place present day extreme events in historical context. Analysis of trends in extreme precipitation in Ireland have largely been limited to the second half of the 20th century due to lack of data availability in digital format. Using recently digitized data, this study provides the first assessment of long‐term changes in extreme daily precipitation observed at 30 locations across Ireland. Quality control of rescued data is carried out before selected long‐term stations are tested for homogeneity using the RHtests software. Details of detected breakpoints and the application of adjustments to daily values are discussed. Eleven extreme precipitation indices are calculated on an annual basis and analysed to determine spatial and temporal trends in the frequency, intensity and magnitude of observed precipitation. The persistence of trends for varying record lengths and for two fixed periods (1910–2019 and 1940–2019) of analysis is assessed for all stations and indices. Results show increases in precipitation intensity, especially notable in the east and southeast of the island. Our findings also show that the contribution of heavy and extreme precipitation events to annual totals is increasing, while there was no persistent trends in annual totals or consecutive wet or dry days. [ABSTRACT FROM AUTHOR]

Convective aggregation refers to the clustering of convective events and occurs on a wide range of spatial scales. It has been suggested that the behavior of convective aggregation may change under global warming, with potential implications for future changes in precipitation extremes. Here, convective regions of the tropics are defined from a percentile threshold on gridded daily precipitation data and used to quantify large‐scale convective aggregation in an ensemble of global climate models. Applying three separate indices for aggregation, it is found that large‐scale convective aggregation increases in 17 of the 19 analyzed models under future warming. However, aggregation is found not to be correlated with tropical‐mean precipitation extremes, either climatologically or with respect to the sensitivity to warming. The large model spread in aggregation indices across the ensemble suggests the possible utility of large‐scale convective aggregation as a target for model evaluation. Plain Language Summary: Rainfall in the tropics is not evenly distributed, rather it occurs in clusters of clouds of various sizes and shapes, from a line of thunderstorms to "superclusters" spanning thousands of km. The size and spatial distribution of these clusters are hypothesized to influence the frequency and intensity of heavy rainfall in the tropics. In this study, we develop a method for quantifying the amount of clustering in the tropics, and we investigate whether it is projected to change in the future in a suite of state‐of‐the‐art climate models. Almost all the models project increases in clustering in the future, but, surprisingly, a model's projection of clustering does not seem to affect its projection of changes to heavy rainfall. The results suggest that effects other than clustering play a dominant role in determining the range of heavy rainfall outcomes projected by climate models. Key Points: According to three separate metrics, large‐scale convective aggregation increases with warming in 17 of 19 global climate models (GCMs) examinedThere is substantial spread in large‐scale convective aggregation behavior among the GCMs consideredNo statistically significant inter‐model relationship between degree of aggregation and tropical‐mean precipitation extremes was found [ABSTRACT FROM AUTHOR]

Ethiopia witnessed tremendous precipitation variability and extremes linked with large-scale climate anomalies. This study investigated the long-term spatiotemporal trends of precipitation extremes, significant change points, and its teleconnection to climate anomalies. We used a daily CHIRPS gridded precipitation dataset of the past four decades covering from 1981 to 2019. Eight extreme precipitation indices are defined here based on Expert Team on Climate Change Detection and Indices guidelines. We used the Mann–Kendall test, Sen's slope estimator, and Pettitt's test to investigate trends of the precipitation change in terms of the magnitude and change point of time series. Wavelet coherence and correlation coefficient are used to identify the relationship between precipitation extremes and climate indices. Our results show a significantly decreasing trend for the Kiremt season (June to Sept) and Belg season (Feb to May) over southeast Ethiopia. The majority of grid points experience a change in time series during 1990 to 2012. Most precipitation extreme indices show an increasing trend over the south and southwest region, except consecutive wet days (CWD), which shows a decreasing trend at similar locations. The multiscale analysis presents strong coherence between precipitation anomaly and Nino 3.4 and IOD over the south and southeast region. Similarly, spatial correlation shows that IOD and Nino 3.4 are positively correlated to R10mm, R25mm, PRCTOT, Rx5day, R95ptot, and R99ptot over south, southwest, and southeast parts of the country. A negative correlation is observed with CDD for similar locations along with NAO climate index for most precipitation extremes. [ABSTRACT FROM AUTHOR]

Extreme precipitation events bring considerable risks to the natural ecosystem and human life. Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management. In this study, daily precipitation data (1957–2019) were collected from 24 meteorological stations in the Weihe River Basin (WRB), Northwest China and its surrounding areas. We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space- time cube (STC), and then predicted precipitation extremes using long short-term memory (LSTM) network, auto-regressive integrated moving average (ARIMA), and hybrid ensemble empirical mode decomposition (EEMD)-LSTM-ARIMA models. The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB. There were two clusters for each extreme precipitation index, which were distributed in the northwestern and southeastern or northern and southern of the WRB. The precipitation extremes in the WRB present a strong clustering pattern. Spatially, the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB, respectively. Hot spots (25.00%–50.00%) were more than cold spots (4.17%–25.00%) in the WRB. Cold spots were mainly concentrated in the northwestern part, while hot spots were mostly located in the eastern and southern parts. For different extreme precipitation indices, the performances of the different models were different. The accuracy ranking was EEMD-LSTM-ARIMA > LSTM > ARIMA in predicting simple daily intensity index (SDII) and consecutive wet days (CWD), while the accuracy ranking was LSTM > EEMD-LSTM-ARIMA > ARIMA in predicting very wet days (R95P). The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes. [ABSTRACT FROM AUTHOR]

Rainfall are subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions can greatly help interpreting rainfall patterns and improve the inference of return levels. In practice, partitioning a region of interest into homogeneous sub-regions is a delicate statistical task, especially in regards to modeling heavy rainfall features.In this work, our main goal is to propose and study a fast and efficient clustering algorithm. Compared to classical regional frequency analysis techniques, a key aspect is that our algorithm does not rely on the a priori choice of covariates. The proposed numerical scheme is only based on the precipitation dataset at hand, including low, moderate and heavy rainfall. In terms of inference, our approach builds on the easy-to-compute and reliable probability weighted moments commonly used in hydrology. While being in compliance with extreme value theory, we do not impose a parametric form on rainfall distributions and, neither thresholding nor block maxima steps are required in our proposed approach. By construction, our clustering method preserves the scale invariance principle of any classical regional frequency analysis.The performance of our clustering algorithm is assessed on a detailed experimental design based on the extended Generalized Pareto distribution.Sensitivity to the number of clusters is carefully analyzed.We apply our clustering algorithm on Switzerland daily precipitation measured at 191 sites. The found homogeneous regions are consistent with local orography and our approach outperforms the classical regional frequency analysis based on normalized elevation and coordinates as covariates. To complete our analysis of Swiss rainfall, we propose three models based on our clustering outputs. A comparison between our local, semi-regional and regional models indicates that a relatively simple model with two clusters and a spatially varying scale parameter can compete very well against complex models.

Precipitation clusters are spatially contiguous precipitating regions. Large clusters in the tropics are rare, extreme events that include organized precipitating systems. Changes to the probability distributions of tropical precipitation clusters under global warming are examined using models from the coupled model intercomparison project Phase 6 (CMIP6). Every analyzed model projects significant increases in frequencies of both very large‐sized clusters and clusters with very large area‐integrated precipitation (cluster power). The occurrence probability for the highest historical cluster power values increases by a factor between 4 and 15 among models in the end‐of‐century SSP5‐8.5 scenario. These changes primarily occur over the precipitating tropics: the western Pacific, Indian subcontinent, central and east Pacific convergence zones, and parts of South America. This spatial pattern is largely explained by Clausius‐Clapeyron scaling of current climate cluster power values. Societal impacts of cluster power increases could be acute in coastal regions of the Indian subcontinent and western Pacific islands. Plain Language Summary: Spatially continuous precipitating points are called precipitation clusters. In the tropics, the largest of these clusters represent systems like hurricanes or groups of thunderstorms. Large clusters also release greater rainfall and therefore hold more potential for destruction. In this study, we use state of the art climate models to examine how precipitation clusters with very large size and rain amounts will change under global warming. All the models we analyzed agree that there will be significant increases in the frequency of large and heavily precipitating clusters. The models project between a 4 and 15 times increase in the frequency of clusters bearing the largest rain amounts. These increases are projected to happen predominantly over tropical regions with warm surface waters. We also found a simple hypothesis that explains these changes. This hypothesis is based on the fact that warmer air can hold more moisture. Increases in the frequency of precipitation clusters with large rain amounts could be particularly devastating for the islands of the western Pacific and heavily populated coastal regions of the Indian subcontinent. Key Points: Probability distributions of precipitation clusters (contiguous precipitating regions) are reasonably reproduced in CMIP6 modelsUnder global warming, models project increases in frequencies of both clusters with large size and clusters with large precipitationThese increases primarily occur over tropical regions with a large incidence of heavily precipitating systems in current climate [ABSTRACT FROM AUTHOR]

Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD). [ABSTRACT FROM AUTHOR]

This study explored the human influence on the observed changes in precipitation extremes across China for the period of 1961–2014 by the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Changes in observations and CMIP6 simulations under different external forcings for annual total wet‐day precipitation (PRCPTOT), the number of heavy precipitation days (R10 mm), very wet days (R95p), extremely wet days (R99p), the maximum 1‐day precipitation amount (RX1day), and the maximum 5‐day precipitation amount (RX5day) are compared using a regularized optimal fingerprinting method. The results show that both anthropogenic (ANT) and greenhouse gas (GHG) experiments generally exhibit increasing trends in precipitation extremes over China, agreeing with the overall observed increases. The ANT influence is robustly detected for R95p, R99p, RX1day, and RX5day over China, with clear separation from the natural forcing. Additionally, the GHG signal is also detectable for R95p, R99p, and RX1day but separated from the natural and other ANT forcings for only R99p and RX1day and from the natural and ANT aerosol forcings for only RX5day. Thus, the intensified precipitation extremes can be attributed to human influence dominated by the GHG effect. However, detections fail for PRCPTOT and R10 mm. The ANT aerosol forcing may partly offset the role of the GHG responses to observed increases despite being undetectable. Based on the detection and attribution results, the observation‐constrained projections indicate that precipitation extremes are projected to significantly increase under the SSP2–4.5 and SSP5–8.5 scenarios but with a smaller magnitude compared to the raw CMIP6 outputs. Plain Language Summary: The human contribution to precipitation extremes across China was analyzed in this study. The observed precipitation extremes generally increase over China during the period of 1961–2014. The changes in human‐induced precipitation extremes are roughly consistent with the observed changes. Based on a detection technique, the human influence can be identified in the changes in the precipitation extremes, which is separable from the role of solar and volcanic activities. The effects of anthropogenic greenhouse gases can also be detected in the observed increases in precipitation extremes over China. Thus, human influence can contribute to intensified precipitation extremes, and greenhouse gas emissions are the dominant contributor. However, precipitation extremes induced by anthropogenic aerosols show decreases across China, and anthropogenic aerosols may make an opposite contribution to the increases in precipitation extremes despite failed detection. The results further show that precipitation extremes over China will significantly increase in the future with a smaller amplitude than those expected from the global climate models in this study. Key Points: Anthropogenic influence, dominated by greenhouse gas emissions, is detectable in intensified precipitation extremes over ChinaAnthropogenic aerosols may partially offset the role of greenhouse gases in the observed increasesThe observation‐constrained projections show smaller increases for precipitation extremes than the raw CMIP6 outputs [ABSTRACT FROM AUTHOR]

Idealized simulations of the tropical atmosphere have predicted that clouds can spontaneously clump together in space, despite perfectly homogeneous settings. This phenomenon has been called self-aggregation, and it results in a state where a moist cloudy region with intense deep convectivestorms is surrounded by extremely dry subsiding air devoid of deep clouds. We review here the main findings from theoretical work and idealized models of this phenomenon, highlighting the physical processes believed to play a key role in convective self-aggregation. We also review the growing literature on the importance and implications of this phenomenon for the tropical atmosphere, notably, for the hydrological cycle and for precipitation extremes, in our current and in a warming climate. [ABSTRACT FROM AUTHOR]

The performance of bias-corrected precipitation of the Coupled Model Intercomparison Project Phase 6 (CMIP6) was evaluated using gauge observations from 45 synoptic stations. The spatial distribution and anomaly of two absolute thresholds of heavy (R10mm), very heavy (R20mm), and two relative thresholds of very wet days (R95p) and extremely wet days (R99p) of precipitation were investigated for the historical period (1975–2014) and two future periods (2021–2060 and 2061–2100) in Iran. Five CMIP6 models were evaluated, including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL by using root mean square error (RMSE), mean bias error (MBE), percent bias (PBIAS), correlation coefficient (PCC) statistics, and receiver operating characteristic (ROC) analysis. An ensemble of the models was generated by applying the independent weighted mean (IWM) method. Among the five models, maximum overestimation and minimum underestimation of precipitation amount are seen in IPSL-CM6A-LR and UKESM1-0-LL, respectively. The maximum R10mm and R20mm computed 35.10 and 18.50 days/year for the historical period, which were identified over the south of the Caspian Sea and Zagros Mountain chain. The anomaly of R10mm and R20mm in all river basins of Iran is positive, except for the four basins of Karkheh, Karun, Tashk-Bakhtegan, and Maharloo for the SSP5-8.5. Precipitation thresholds for both indices will be increasing over the coming decades on the coast of the Caspian Sea, while the index for R95p in the western and southwestern regions and R99p in the southern regions of Iran will experience a decreasing trend. [ABSTRACT FROM AUTHOR]

This study explores the model performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating precipitation extremes over the mid–high latitudes of Asia, as compared with predecessor models in the previous phase, CMIP5. Results show that the multimodel ensemble median generally outperforms the individual models in simulating the climate means of precipitation extremes. The CMIP6 models possess a relatively higher capability in this respect than the CMIP5 models. However, discrepancies also exist between models and observation, insofar as most of the simulated indices are positively biased to varying degrees. With respect to the temporal performance of indices, the majority are overestimated at most time points, along with large uncertainty. Therefore, the capacity to simulate the interannual variability needs to be further improved. Furthermore, pairwise and multimodel ensemble comparisons were performed for 12 models to evaluate the performance of individual models, revealing that most of the new-version models are better than their predecessors, albeit with some variance in the metrics amongst models and indices.

Several studies showed the impact of global climate change in Germany and Saxony including the risk of increasing precipitation extremes. Here, heavy precipitation was analyzed on the basis of daily precipitation sums using the 95th percentile (index R95p). The long term development was studied for selected stations (1917–2013). Transects with high spatial resolution (1×1 km) (1961–2015) complemented the study to gain information about spatial temporal development of the occurrence of precipitation extremes. The non parametric kernel occurrence rate estimation has been applied to reveal changes in the temporal development of daily totals. The most distinct changes have been found for the seasons and the growing seasons and only slight changes for the calendar year and the meteorological half-years. The findings of this study showed a shifting seasonality with decreasing number of heavy precipitation events in the growing season I (April, May, June) and increasing number of events in growing season II (July, August, September). Furthermore, a distinct periodicity has been revealed in all findings for all seasons, particularly striking in the growing seasons, indicating the influence of large scale drivers as potentially the North Atlantic Oscillation on local precipitation extremes. Our data showed, that kernel occurrence rate estimation is a suitable approach to analyze the temporal development of heavy precipitation with a high temporal and spatial resolution.

Precipitation extremes are among the most dangerous climate-related hazards over China, and they are expected to significantly increase in the future in both frequency and intensity. Exposure to precipitation extremes and changes therein are determined by extreme events and the corresponding population changes. Here, the authors analyze the changing population exposure across China in the future using ensembles of high-resolution simulations with RegCM4 and population scenarios. The authors find that aggregate exposure over China increases by nearly 21.6% under the RCP4.5-SSP2 scenario by the end of this century, although populations are projected to decrease. East China will experience the largest absolute increase in exposure from 424 million person-events to 546 million person-events, while the Tibetan Plateau region will experience the largest relative increase of nearly 44.4%. This increase in exposure mainly results from the climate effect contribution. Further assessments indicate that the exposure increase over China does not rely on the greenhouse gas emissions and population growth scenarios, but the higher emissions scenario generally leads to higher exposure regardless of population growth, highlighting the efficacy of mitigation efforts in reducing exposure to precipitation extremes.

Abstract This study explored the human influence on the observed changes in precipitation extremes across China for the period of 1961–2014 by the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Changes in observations and CMIP6 simulations under different external forcings for annual total wet‐day precipitation (PRCPTOT), the number of heavy precipitation days (R10 mm), very wet days (R95p), extremely wet days (R99p), the maximum 1‐day precipitation amount (RX1day), and the maximum 5‐day precipitation amount (RX5day) are compared using a regularized optimal fingerprinting method. The results show that both anthropogenic (ANT) and greenhouse gas (GHG) experiments generally exhibit increasing trends in precipitation extremes over China, agreeing with the overall observed increases. The ANT influence is robustly detected for R95p, R99p, RX1day, and RX5day over China, with clear separation from the natural forcing. Additionally, the GHG signal is also detectable for R95p, R99p, and RX1day but separated from the natural and other ANT forcings for only R99p and RX1day and from the natural and ANT aerosol forcings for only RX5day. Thus, the intensified precipitation extremes can be attributed to human influence dominated by the GHG effect. However, detections fail for PRCPTOT and R10 mm. The ANT aerosol forcing may partly offset the role of the GHG responses to observed increases despite being undetectable. Based on the detection and attribution results, the observation‐constrained projections indicate that precipitation extremes are projected to significantly increase under the SSP2–4.5 and SSP5–8.5 scenarios but with a smaller magnitude compared to the raw CMIP6 outputs.

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

Over the course of a season, a location’s precipitation is comprised of moisture sourced from a diverse set of geographic regions. Seasonal extremes in precipitation may arise from changes in the contribution of one or several of these sources. Here, we use the Community Earth System Model with numerical water tracers to quantify the contribution of locally sourced, known as precipitation recycling, versus remotely sourced precipitation to seasonal wet and dry extremes across North America. The greatest impact of recycling on both wet and dry extremes is found in the Interior West of the United States where changes to recycling contribute as much as 25%–30% of drought deficit and pluvial surplus. Recycling contributions are smaller across the eastern U.S., generally less than 8%, highlighting the greater role of imported moisture for explaining hydroclimate extremes in these regions. Robust contributions of precipitation recycling to drought and pluvials across the Interior West are driven by consistent changes to local evaporation and the conversion of local evaporation to local precipitation during extreme hydroclimate conditions. The results are consistent with an energy-limited and water-limited evaporation framework and provide a new estimate of the role of local processes in shaping hydroclimate extremes.

Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.

Tropical cyclones (TCs) cause compound extremes of rainfall and wind gust. However, their occurrence and impacts on India still need to be better understood. Using ERA5 reanalysis and cyclone eAtlas, we examine the compound extremes of precipitation and wind gust driven by TCs that made landfall over India during 1981–2021. Based on the joint return period of compound extremes, the five worst TCs occurred in May 1990, May 1999, May 2010 (Laila), October 2014 (Hudhud), and May 2020 (Amphan). A majority of TCs during 1981–2021 originated from the Bay of Bengal (BoB) and only a few from the Arabian Sea (AS). While the frequency of all the TCs has either declined or remained stable in the North Indian Ocean (NIO, BoB, AS) during 1981–2021, the frequency of TCs with compound extremes has increased by about three-fold during the most recent decade (2011–2021). Compound extremes driven by TCs affect large regions along the coast and risk infrastructure and human lives. The frequency of TCs with large area of impact (greater than 200 000 km ^2 ) compound wind and precipitation extreme extent exhibits a three-fold rise during 1981–2021, indicating an increase in the hazard associated with the compound extremes driven by TCs in India.

It is assumed that extreme precipitation (P) increases with air temperature (T) by a scaling rate close to 7%/°C without moisture limitation according to the Clausius-Clapeyron (C-C) relationship. However, the spatial distribution of the P-T relationship in China is subject to divergent conclusions including both sub-C-C (7%/°C) scaling with reasons yet to be examined. Based on the long-term observations, here we show that P-T relationships with peak structure exist in most regions across China. The scaling rate in the wet season shows a decreasing spatial pattern from the southeast to the northwest, while sub-C-C scaling in the dry season dominates most regions across China. Mixing precipitation events from different seasons could lead to miscalculation of the P-T scaling rate. Furthermore, significant increases in peak precipitation at high percentiles have been observed in southern regions of China during the historical period, indicating that the peak structure does not imply a potential upper limit for precipitation extremes. Our results highlight the importance of considering seasonal characteristics in analyzing the extreme precipitation-temperature relationship in a changing climate.

Global warming has altered the energy budget and water cycle processes of the land–atmosphere system, which has resulted in significant effects on precipitation extremes. Previous studies have identified a hook structure between near-surface temperature and precipitation extremes, in which extremes increase with temperature rises and decline thereafter. However, the underlying physical mechanisms of this association remain poorly understood. In this study, global-scale responses of precipitation extremes to near-surface air temperature (SAT) and dew point temperature (DPT) were quantified using the ERA5 reanalysis dataset. The results reveal a hook structure between precipitation extremes scaling and temperature, for both SAT and DPT, over many regions worldwide. The peak point temperature ( T _pp ) ranges from 15 °C to 25 °C, increasing as latitude decreased. The association of precipitation extremes with SAT is negative in many areas in the tropics, whereas that with DPT is almost always positive; this suggests that moisture supply is the main factor limiting precipitation at higher surface temperatures. The hook structure and scaling rates incompatible with Clausius–Clapeyron scaling are associated with various factors including precipitation duration, total column water vapour, convective available potential energy, and relative humidity.

Extreme precipitation intensities derived from temporally aggregated time series can be considerably underestimated. Therefore, some form of correction is appropriate prior to their usage e.g. for the derivation of idf curves. The correction is usually performed in a multiplicative manner, using the coefficient obtained as a mean ratio of real to aggregated extremes. In this paper a novel correction approach is derived, allowing an individual treatment of each aggregated extreme. The precipitation time series from Prague (central Europe) are used for the assessment of newly introduced method. The comparison with the standard approach shows that the new method reaches better results as it reduces an undesirable overestimation of the corrected extremes. Moreover, the effect of data aggregation on extreme precipitation intensities is evaluated as well as the effect on quantiles of the generalized extreme value (GEV) distribution. The results are compared to those published for other climatic conditions. [ABSTRACT FROM AUTHOR]

The urban heat island is a representative urban climate characteristic, which can affect heat-stress conditions and extreme precipitation that are closely connected with human life. Better understanding of urban-climate interactions, therefore, is crucial to ultimately support better planning and adaptation in various application fields. This study assesses urban-climate interactions during summer for eastern North America using regional climate model simulations at 0.22° resolution. Two regional climate model experiments, with and without realistic representation of urban regions, are performed for the 1981–2010 period. Comparison of the two experiments shows higher mean temperatures and reduced mean precipitation in the simulation with realistic urban representation, which can be attributed primarily to reduced albedo and soil moisture for the urban regions in this simulation. Furthermore, the mean temperature and precipitation in the simulation with improved urban representation is also closer to that observed. Analysis of short-duration precipitation extremes for climatologically different sub-regions, however, suggests that, for higher temperatures, the magnitudes of precipitation extremes are generally higher in the simulation with realistic urban representation, particularly for coastal urban regions, and are collocated with higher values of convective available potential energy and cloud fraction. Enhanced sea and lake breezes associated with lower sea level pressure found around these regions, contribute additional water vapor and further enhance dynamic convective development, leading to higher precipitation intensities. Analysis of temperature extremes clearly demonstrates that urban regions experience aggravated heat-stress conditions due to relatively higher temperatures despite reduced relative humidity. Double the number of extreme heat spells lasting six or more days are noted for the coastal urban regions in the study domain. This study, in addition to demonstrating the differences in urban-climate interactions for climatologically different regions, also demonstrates the need for better representation of urban regions in climate models to generate realistic climate information. [ABSTRACT FROM AUTHOR]

East Africa is so vulnerable to the impacts of precipitation extremes varying from frequent floods to prolonged droughts. However, systematic regional assessment of precipitation extremes across seasons has received little attention, and most previous studies of precipitation extremes have employed many indices and sparse gauge observations giving marginalized details. In this study, we use three precipitation extreme indices to examine the intensity of the highest single-day rainfall record (rx1day), prevalence of very heavy rainfalls (r30mm), and persistence of successive wet days (cwd) at both annual and seasonal scales over recent two decades (1998–2018) based on the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis data. The results show that the most intensive and frequent precipitation extremes are noticeable from January to May across the areas extending from Madagascar to the Tanzanian coastal zone. These areas also exhibit patches of significant increasing trends in frequency, duration, and intensity of precipitation extremes annually and seasonally. However, significant declines in frequency and intensity of precipitation extremes are observed from western Ethiopia to Congo-Uganda, especially in June-September. The October–December season witnesses higher interannual variability amounting to overall weak and less significant trends. Further subregional assessment shows overall declining intensity and frequency of precipitation extremes in northern part of the study areas. Mean wetness duration increased, with persistence of moderate wet days and slight reduction of severe events. This study unveils higher susceptibility of the East African region to the widely observed hotspots of precipitation extremes posing threats to food security, water resource, and human well-being. The region should consider upscaling irrigation schemes in addition to planning resilient and supportive infrastructures to withstand the upsurging precipitation extremes, especially along the coastal zone. [ABSTRACT FROM AUTHOR]

This work explores the effect of convective self‐aggregation on extreme rainfall intensities through an analysis at several stages of the cloud lifecycle. In addition to increases in 3‐hourly extremes consistent with previous studies, we find that instantaneous rainrates increase significantly (+30%). We mainly focus on instantaneous extremes and, using a recent framework, relate their increase to increased precipitation efficiency: the local increase in relative humidity drives larger accretion efficiency and lower re‐evaporation. An in‐depth analysis based on an adapted scaling for precipitation extremes reveals that the dynamic contribution decreases (−25%) while the thermodynamic is slightly enhanced (+5%) with convective self‐aggregation, leading to lower condensation rates. When the atmosphere is more organized into a moist convecting region and a dry convection‐free region, deep convective updrafts are surrounded by a warmer environment which reduces convective instability and thus the dynamic contribution. The moister boundary‐layer explains the positive thermodynamic contribution. The microphysic contribution is increased by +50% with aggregation. The latter is partly due to reduced evaporation of rain falling through a moister near‐cloud environment, but also to the associated larger accretion efficiency. Thus, a potential change in convective organization regimes in a warming climate could lead to an evolution of tropical precipitation extremes significantly different than that expected from thermodynamical considerations. The relevance of self‐aggregation to the real tropics is still debated. Improved fundamental understanding of self‐aggregation, its sensitivity to warming and connection to precipitation extremes, is hence crucial to achieve accurate rainfall projections in a warming climate. Plain Language Summary: Heavy precipitation and floods are frequent in the tropics. The spatial organization of weather systems is often associated with these events. Our study investigates the case of convective self‐aggregation which is a particular type of cloud systems' organization observed in idealized numerical simulations. We find that convective self‐aggregation tends to increase rainfall intensities by 30%–70%. There are several processes involved in the formation of heavy rainfall: vertical motion air, condensation into cloud droplets, growth of these droplets into precipitating drops, collection of other cloud‐droplets through their descent and partial re‐evaporation between cloud base and the ground. We examine the contribution of each of these processes and find that the increase in rain rates with convective self‐aggregation is related to both lower rain re‐evaporation and more efficient cloud droplet collection by rain drops through their descent. It is still unclear how heavy rainfall will evolve in a warming climate. While the relationship between temperature and water vapor suggests that heavy rainfall will increase by 7% per 1K, our result shows that a hypothetic change in the organization of weather systems could potentially lead to more dramatic changes in heavy rainfall in a future warming climate. Key Points: Convective self‐aggregation may increase both accumulated and instantaneous rainfall extremesThis increased precipitation is related to reduced rain re‐evaporation and enhanced accretion efficiency with convective self‐aggregationExtreme convective updrafts within self‐aggregated convection are weaker due to warmer environment [ABSTRACT FROM AUTHOR]

Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value (GEV) distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within ±10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions. Plain Language Summary: Annual maxima of daily precipitation are widely used to design critical infrastructures such as dams and stormwater networks. Climate change is expected to increase the frequency and intensity of extreme events. Climate model projections offer a glimpse into the future and can help assess potential changes and impacts. It is reasonable to assume that climate models simulating accurately the historical climate may also simulate well the future. Here, we assessed the latest generation of climate models, that is the CMIP6 models, to reproduce the historical annual maximum daily precipitation. We compared simulations and observations of extreme precipitation using advanced summary statistics, novel probability similarity measures, and robust statistical tests. The results indicate that models, in general, reproduce well the behavior of annual maximum precipitation, but biases exist especially in the simultaneous behavior of mean and variance. Shortcomings of CMIP6 models are highlighted in the Arctic, Tropics, arid, and semi‐arid regions. Key Points: Assessment of CMIP6 biases in precipitation extremes using L‐moments, novel probability similarity measures and statistical testsCMIP6 models reproduce fairly well observed annual maximum precipitation but biases exist in the joint behavior of mean and variabilityShortcomings of CMIP6 models are highlighted in the Arctic, Tropics, arid, and semi‐arid regions [ABSTRACT FROM AUTHOR]

Future intensification of Amazon drought resulting from climate change may cause increased fire activity, tree mortality, and emissions of carbon to the atmosphere across large areas of Amazonia. To provide a basis for addressing these issues, we examine properties of recent and future meteorological droughts in the Amazon in 35 climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that the CMIP5 climate models, as a group, simulate important properties of historical meteorological droughts in the Amazon. In addition, this group of models reproduces observed relationships between Amazon precipitation and regional sea surface temperature anomalies in the tropical Pacific and the North Atlantic oceans. Assuming the Representative Concentration Pathway 8.5 scenario for future drivers of climate change, the models project increases in the frequency and geographic extent of meteorological drought in the eastern Amazon, and the opposite in the West. For the region as a whole, the CMIP5 models suggest that the area affected by mild and severe meteorological drought will nearly double and triple, respectively, by 2100. Extremes of wetness are also projected to increase after 2040. Specifically, the frequency of periods of unusual wetness and the area affected by unusual wetness are projected to increase after 2040 in the Amazon as a whole, including in locations where annual mean precipitation is projected to decrease. Our analyses suggest that continued emissions of greenhouse gases will increase the likelihood of extreme events that have been shown to alter and degrade Amazonian forests.

The study analyzes the consistency of future precipitation extremes projected over China under moderate climate scenarios (RCP4.5 and SSP2-4.5) and extreme climate scenarios (RCP8.5 and SSP5-8.5), based on best models' ensembles for each extreme index (best-MME), best models' ensembles regarding all indices (best-rankMME), and multi-model ensembles (MME). To select the best models, the integrated quadratic distance (IQD) combined with the Taylor skill score are used. This evaluation is a practical example of the benefits of using IQD to evaluate the models compared with root mean square error since the former is not sensitive to the similarity of the distribution's averages. All ensembles in each scenario consistently project the decrease of consecutive dry days (CDD) periods in western and northeast China, the increase of the number of days with heavy precipitation (R10mm) over the Qinghai-Tibet Plateau, and the increase of the precipitation intensity (SDII) in most of the Chinese territory. The moderate and extreme scenarios project very similar climate change patterns but more extreme scenarios show greater climate change signal magnitudes. In the case of the moderate scenarios, the most significant uncertainty relies on the CDD projections over southeastern China, where best-rankMME and MME project increasing CDD in the future whilst best-MME project decreasing CDD, although these changes are not statistically significant. Regarding extreme scenarios, the most significant uncertainty relies on the R10mm projections over southeastern China, where the best-MME CMIP5 projects decreasing R10mm (not statistically significant), whilst all the remaining ensembles project increasing values.

The southeast coastal region of China is susceptible to challenges related to extreme precipitation events; hence, projection of future climate extremes changes is crucial for sustainable development in the region. Using the Regional Climate Model Version 4 (RegCM4), the future changes of summer precipitation extremes have been investigated over the Jiulongjiang River Basin (JRB), a coastal watershed in Southeast China. Comparison between the RegCM4 simulated and observed rainy season precipitation over JRB suggests that the RegCM4 can reasonably reproduce the seasonal precipitation cycle, the frequency distribution of precipitation intensity, and the 50-year return levels of precipitation extremes over JRB. Furthermore, the model projects an increase in daily maximum rainfall (RX1day) mostly over the northern part of the basin and a decrease over other parts of the basin, while projecting a widespread decrease for maximum consecutive 5-day precipitation amount (RX5day) relative to the present day. In terms of the 50-year return level of RX1day (RL50yr_RX1day), a general increase is projected over most parts of the basin in the near and far future of the 21st century, but a decrease can be found in the northeast and southwest parts of the JRB in the mid-21st century. The future change of the 50-year return level of RX5day (RL50yr_RX5day) shows a similar spatial pattern with that of RL50yr_RX1day in the near and mid-21st century, but with a larger magnitude. However, a remarkable decrease in RL50yr_RX5day is found in the south basin in the far future. Meanwhile, the projected changes in the 50-year return level for both RX1day and RX5day differ between the first and second rainy seasons in JRB. Specifically, the future increase in RL50yr_RX5day over the north basin is mainly contributed by the changes during the first-half rainy season, while the decrease of RL50yr_RX5day in the south is mostly ascribed to the future changes during the second-half rainy season. All above results indicate that the future changes of precipitation extremes in JRB are complicated, which might differ from extreme indices, seasons, and future projected periods. These will thus be of practical significance for flood risk management, mitigation, and adaptation measures in Jiulongjiang River Basin.

Abstract This work explores the effect of convective self‐aggregation on extreme rainfall intensities through an analysis at several stages of the cloud lifecycle. In addition to increases in 3‐hourly extremes consistent with previous studies, we find that instantaneous rainrates increase significantly (+30%). We mainly focus on instantaneous extremes and, using a recent framework, relate their increase to increased precipitation efficiency: the local increase in relative humidity drives larger accretion efficiency and lower re‐evaporation. An in‐depth analysis based on an adapted scaling for precipitation extremes reveals that the dynamic contribution decreases (−25%) while the thermodynamic is slightly enhanced (+5%) with convective self‐aggregation, leading to lower condensation rates. When the atmosphere is more organized into a moist convecting region and a dry convection‐free region, deep convective updrafts are surrounded by a warmer environment which reduces convective instability and thus the dynamic contribution. The moister boundary‐layer explains the positive thermodynamic contribution. The microphysic contribution is increased by +50% with aggregation. The latter is partly due to reduced evaporation of rain falling through a moister near‐cloud environment, but also to the associated larger accretion efficiency. Thus, a potential change in convective organization regimes in a warming climate could lead to an evolution of tropical precipitation extremes significantly different than that expected from thermodynamical considerations. The relevance of self‐aggregation to the real tropics is still debated. Improved fundamental understanding of self‐aggregation, its sensitivity to warming and connection to precipitation extremes, is hence crucial to achieve accurate rainfall projections in a warming climate.

Abstract Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value (GEV) distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within ±10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions.

The occurrence of several precipitation extremes over sub-seasonal time windows can have major impacts on human societies, leading for instance to floods. Here, we apply a simple statistical framework based on Ripley’s K function, at a global scale and for each season separately, to identify regions where precipitation extremes tend to cluster in time over timescales of a few days to a few weeks. We analyze several observational and reanalysis datasets, as well as output from CMIP6 Global Climate Models (GCMs). Good agreement is found on the spatio-temporal clustering patterns across datasets. Sub-seasonal temporal clustering is largely concentrated over the tropical oceans, where it can be detected year-round. It is also significant over certain tropical lands, like Eastern Africa, and seasonally outside the tropics in several regions, most notably around the eastern subtropical oceans (Iberian Peninsula and Western North America during the DJF and MAM seasons) Southwest Asia (especially during JJA and SON) and Australia (in SON). We also find that CMIP6 models generally correctly reproduce clustering patterns, paving the way for an assessment of trends in sub-seasonal clustering under climate change. Clustering of present-day extremes increases in many areas under climate change. Changes diagnosed by comparing present day and future extreme percentiles are positive and negative and strongest in the tropical areas.

This article analyzes recent trends in extreme precipitation in the Southwestern Alps and compares these trends to changes in the occurrence of the atmospheric influences generating extremes. We consider a high-resolution precipitation dataset of 1 × 1 km2for the period 1958–2017. A robust method of trend estimation in extreme precipitation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting trends in extreme precipitation depending on the season. In autumn, most of Southern France shows significant increasing trends, with increase in the 20-year return level between 1958 and 2017 as large as its average value over the period, while the Northern French Alps and the Swiss Valais show decreasing extremes. In winter, significant increasing extremes are found in the valleys and medium mountain areas surrounding the Northern French Alps, while the inner French Alps, the Swiss Valais and the Aosta Valley show significant decreasing trends. In the other seasons, the significant trends are mostly negative in the Mediterranean area. Comparing these trends to changes in the occurrence of the atmospheric influences generating extremes shows that part of the significant changes in extremes can be explained by changes in the dominant influences, in particular in the Mediterranean influenced region that shows the most organized trends. In particular, the strong positive trends in extreme precipitation in autumn in Southern France are concomitant with an increase in Mediterranean influence generating extremes.

Based on the outputs of the global climate models (GCMs) HadGEM2-ES, NorESM1-M and MPI-ESM-LR from Coupled Model Intercomparison Project Phase 5 (CMIP5) and the downscaling results with the regional climate model (RCM) REMO, the ability of the climate models to reproduce the extreme precipitation in China during the current period (1986–2005) is evaluated. Then, the future extreme precipitation in the mid (2036–2065) and the late 21st century (2066–2095) is projected under the RCP8.5 scenario. The results show that the RCM simulations have great improvements compared with the GCMs, and the ensemble mean of the RCM results (ensR) outperforms each single RCM simulation. The annual precipitation of the RCM simulations is more consistent with the observation than that of the GCMs, with the overestimation of the peak precipitation reduced, and the ensR further reduces the bias. For the extreme precipitation, the RCM simulations significantly decrease the underestimation of intensity in the GCMs. The RCM simulations and the ensR can greatly improve the simulations of Rx5day and CWD compared with the GCMs, decreasing the wet bias in North China and Northwest China. In the future, the consecutive dry days (CDD) will decrease in the northern arid regions, especially in North China and Northeast China. However, the southern regions will experience longer dry period. Both the amount and the intensity of precipitation will increase in various regions of China. The number of wet days will decrease in the south and increase in the north area. The significantly greater Rx5day and R95t indicate more intensive extreme precipitation in the future, and the intensity in the late 21st century will be stronger than that in the middle. Attribution analysis indicates that the extreme precipitation indices especially the R95t have significant positive temporal and spatial correlations with the water vapor flux.

Flash droughts are defined as fast and intense dryings of the land system. In these episodes, declines in precipitation deficits are often accompanied by rapid intensifications of evaporative demand, leading to sharp soil moisture decreases and noticeable agricultural and environmental impacts. This research presents a straightforward framework for global historical characterization of precipitation-deficit-related flash droughts, examining the extent to which this type of hazard can be described using only pentad rainfall data. The Drought Exceedance Probability Index was applied to global gridded high-resolution rainfall data for 1979 - 2020. Sharp upsurges in the pentad index series were detected and counted to analyze the occurrence of precipitation-deficit flash droughts. The precipitation characteristics associated to flash drought incidence were explored to learn if some rainfall regimes or times of the year are more prone to the phenomenon, which could help societies become more prepared for the risk. It was observed that climates with marked inter-annual and intra-annual rainfall variability record more flash droughts, especially when that variability is significant during the local wet seasons. This is the case of regions with erratic rainfall generation mechanisms such as Mediterranean climates or monsoon climates. The episodes mainly occur during what is expected to be the humid time of the year, when they can produce greater impact. The methodology used was able to detect the most intense events described in previous studies that used variables associated to soil moisture dryness, confirming the role of acute precipitation deficits in triggering them. [ABSTRACT FROM AUTHOR]

The frequency and spatial distributions of precipitation extremes (PEs) and deep convective clouds (DCC) across the Amazon region were assessed using satellite-derived data. For PEs, CHIRPS dataset for the period 1981–2018 were used to calculate a set of absolute, threshold, duration, and percentile-based threshold indices defined by the Expert Team on Climate Change Detection and Indices. DCC occurrence was assessed based on the Advanced Microwave Sounding Unit data for the period 2002–2018. In northern Amazon (north of 5 ∘ S ) PEs and DCC are more frequent ( ≥ 60 % frequency) during February–June. Averaged trends over these months have shown increase in daily rainfall above 20 mm of near 3 days over the 1981–2018 period, and an increase of 2 consecutive wet days (P ≥ 1 mm ) in the same period. South of 5 ∘ S prevalence of PEs and DCC is largely observed during November–March ( ≥ 60 % frequency), whereas the longest persistence of dry days is observed during June–August. Though all PE trends point to an intensification of rainfall in November–March, only consecutive dry days in winter (JJA) and spring (SON) show significant trends, pointing to an increase of 7 days over the 38-yr winters. Rainfall extremes over the entire Amazon region were found to be moderate to strongly correlated with the mean vertically integrated moisture divergence, and in southern Amazon also to upper level divergence and upward vertical velocity. Increased frequency of DCC were found over the whole basin ( ∼ 18 % yr - 1 ), in contrast to decreased convective overshooting (up to ∼ 15.4 % yr - 1 ). [ABSTRACT FROM AUTHOR]

Under climate change, increases in precipitation extremes are expected due to higher atmospheric moisture. However, the total precipitation in an event also depends on the condensation rate, precipitation efficiency, and duration. Here, a new approach following an "ingredients‐based methodology" from severe weather forecasting identifies important aspects of the heavy precipitation response to climate change, relevant from an impacts perspective and hitherto largely neglected. Using 2.2 km climate simulations, we show that a future increase in precipitation extremes across Europe occurs, not only because of higher moisture and updraft velocities, but also due to slower storm movement, increasing local duration. Environments with extreme precipitation potential are 7× more frequent than today by 2100, while the figure for quasi‐stationary ones is 11× (14× for land). We find that a future reduction in storm speeds, possibly through Arctic Amplification, could enhance event accumulations and flood risk beyond expectations from studies focusing on precipitation rates. Plain Language Summary: Intense rainstorms are expected to be more frequent due to global warming, because warmer air can hold more moisture. Here, using very detailed climate simulations (with a 2.2 km grid), we show that the storms producing intense rain across Europe might move slower with climate change, increasing the duration of local exposure to these extremes. Our results suggest such slow‐moving storms may be 14× more frequent across land by the end of the century. Currently, almost‐stationary intense rainstorms are uncommon in Europe and happen rarely over parts of the Mediterranean Sea, but in future are expected to occur across the continent, including in the north. The main reason seems to be a reduced temperature difference between the poles and tropics, which weakens upper‐level winds in the autumn, when these short‐duration rainfall extremes most occur. This slower storm movement acts to increase rainfall amounts accumulated locally, enhancing the risk of flash floods across Europe beyond what was previously expected. Key Points: Following an ingredients‐based method, future changes in intense rainstorms in Europe are studied using convection‐permitting simulationsEnvironments favoring high rainfall rates are projected to be 7× more frequent by 2100, while the figure for quasi‐stationary ones is 11×Reduction in storm speeds due to weaker jets, possibly via Arctic Amplification, can enhance accumulations further increasing flood risk [ABSTRACT FROM AUTHOR]

Focussing on the major cities of Brisbane, Sydney, and Melbourne in southeast Australia, this study seeks to determine the environmental factors that distinguish between heavy rainfall events (HREs) and extreme rainfall events (EREs). Using daily rain gauge observations, HREs and EREs are defined for each domain based, respectively, on the 95th and 99th percentiles of wet‐day rainfall for the period 1979–2018. K‐means clustering is applied to mean sea‐level pressure, data from ERA5 to obtain a set of representative large‐scale circulation patterns associated with these events. Composite synoptic maps, mean vertical profiles, and a series of column‐integrated diagnostics are then examined for each cluster and used to compare the environmental characteristics of HREs and EREs. For all three cities, HREs are associated with an upper‐level trough to the west, with large‐scale ascent, positive column water vapour (CWV) anomalies, and strong moisture transport over the analysis domain. For Brisbane and Sydney, the clusters are characterised by a coastal trough/low with moist onshore flow from the Tasman and Coral Seas. For Melbourne, circulation patterns are more distinct, with clusters characterised by a front, a cut‐off low, and an inland trough. Compared with HREs, EREs show a more amplified upper‐level trough, with stronger vertical motion and larger CWV anomalies over the analysis domain. In Brisbane and Sydney, EREs also feature stronger and deeper onshore flow, promoting enhanced moisture transport. A diagnostic termed upward vapour transport, which combines the key ingredients of high CWV and large‐scale ascent, is shown to discriminate well between HREs and EREs in all three domains. In contrast, surface temperature, which is frequently linked to rainfall extremes via Clausius–Clapeyron scaling, shows significant overlap between the different event categories, particularly for Brisbane and Sydney. [ABSTRACT FROM AUTHOR]

There is a large agreement that global warming induces changes of precipitation regimes of different nature and amplitude depending on the timescale considered. This question is of special concern regarding extreme rainfall that might have critical socio-environmental consequences. A unified framework is proposed here for detecting trends in extreme rainfall. It is based on the GEV distribution, whose parameters depend both on a simple scaling formulation to account for multiple time durations of rainfall and on time to account for the non-stationarity deriving from climatic trends. The implementation of the model is illustrated in the Sahel region by analyzing 30 in situ rainfall series of 28 years measured at time-steps from 2 to 24 h. While the separate analysis of the point series proves inconclusive for detecting trends at any of the time-steps considered, the inclusion of all the series and time-steps into the proposed unified model allows trends to be detected at a high level of confidence (p-value < 1%). This trend essentially appears in the scale parameter of the regional GEV distribution, involving a 15 to 20% increase of the 10-year rainfall in 28 years, and a 23 to 30% increase of the 100-year rainfall. The main advantages of the proposed framework are (i) its parsimony, allowing for reducing the uncertainty associated with the model inference; (ii) its capacity for detecting trends either in the mean and/or in the variability of the extreme events; and (iii) its ability for producing non-stationary Intensity-Duration-Frequency curves that are coherent over a range of durations of accumulation. [ABSTRACT FROM AUTHOR]

The recently released NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is a high-resolution daily downscaled dataset derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. We comprehensively evaluated the performance of the NEX-GDDP dataset in simulating the characteristics of the climatological precipitation and extreme precipitation events across China from 1960 to 2014. We present here the projected changes in precipitation at the end of the 21st century (2070–2099) with respect to a reference period (1985–2014). We compared the NEX-GDDP dataset with both a state-of-the-art gauge data analysis system and the CMIP6 global climate models. The NEX-GDDP dataset significantly outperformed the CMIP6 simulations in replicating the climatological patterns of precipitation. It showed a closer alignment with the observed data, as evidenced by notably increased correlation coefficients and reduced model-relative errors. The NEX-GDDP dataset exceled in accurately reproducing the spatial distribution of indices of extreme precipitation, surpassing the performance of the CMIP6 simulations. The projections show an increased frequency of heavy precipitation under higher emission scenarios in both datasets, with the CMIP6 simulations consistently estimating a higher probability of heavy rain than the NEX-GDDP dataset. • The NEX-GDDP dataset can well describe the climatology but not trend for observed precipitation. • The NEX-GDDP dataset is also represent the spatial distribution of precipitation extremes than the CMIP6 simulations. • The CMIP6 simulations project a higher probability of heavy rain than the NEX-GDDP dataset. [ABSTRACT FROM AUTHOR]

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s • The Grell convective scheme applied in RegCM4 has been successful in simulating precipitation extremes in Iran. • The RegCM4 has been able to capture precipitation extremes well in Iran. • Consecutive dry days (CDD) are increasing in most parts of Iran. [ABSTRACT FROM AUTHOR]

The daytime and nighttime precipitation proportions of daily total precipitation (especially extreme daily precipitation) are important indicators that help to understand the process of precipitation formation, which in turn helps to evaluate and improve models and reanalysis precipitation data. In this study, we used the Huai River Basin (HRB) as a case to explore the daytime and nighttime precipitation proportions of daily total precipitation based on 135 meteorological stations during 1961–2018. The total, daytime, and nighttime precipitation showed zonal distributions with high and low values in the southern and northern parts of the basin, respectively. The nighttime precipitation was slightly greater than the daytime precipitation. With the increase in precipitation intensity, the seasonal cycles of the total, daytime, and nighttime precipitation were more distinct, and precipitation mainly occurred in summer. The annual range of precipitation differences between daytime and nighttime in wet seasons showed a downward trend in 1961–2003 followed by an upward trend in 2003–2018. This reversal of annual range of precipitation around 2003 may be related to the changes in annual range of convective precipitation differences between daytime and nighttime in wet seasons. The decrease of light precipitation mainly depended on the decrease of nighttime precipitation. The contributions of nighttime precipitation events to torrential precipitation events were greater than those of daytime precipitation. The days of extreme precipitation events accounted for a very low proportion of total precipitation days, but their precipitation amount accounted for relatively high proportions of total precipitation amount. Annual extreme precipitation amount showed a slightly upward trend, which was caused by the increased nighttime precipitation. Under extreme precipitation conditions, large proportions of daytime precipitation were mainly concentrated in the southeastern parts of the HRB, whereas large proportions of nighttime precipitation were mainly concentrated in the northwestern parts of the basin. The concurrent daytime and nighttime precipitation showed slightly increasing trends, especially in the southeastern part of the basin. With the increase in daytime and nighttime precipitation, the risk of concurrent precipitation extremes in the southern part of the basin increased (shorter return period means higher risk).

Precipitation extrema over the Barents Sea and the neighbouring locations in Europe were analysed using data obtained from station observations and a highly detailed ERA5 re-analysis dataset. These data did not always spatially coincide (on average, coincidence was ~50%). Daily amounts of precipitation were typically higher in the observation data, although there may be a reverse picture. The analysis revealed that at several stations and in many of the ERA5 grids, the set of precipitation extremes exists as a mixture of two different subsets. The cumulative distribution functions (CDF) of the largest population in the context of both the re-analysis and observational data are well described by Pareto’s law. However, very rare cases exist in which the values deviate and exceed this base distribution value in regions possessing large values. These super-large anomalies do not obey the statistical law common to all other extremes. However, this does not mean that the extremes can be arbitrarily large. They do not exceed the marginal values that are typical for this type of climate and season. The analysis confirms that extreme precipitation in the western sector of the Arctic is caused by the penetration of moist air masses from the Atlantic in the circulation systems of intense cyclones. At certain times, mesoscale convective systems are embedded in atmospheric fronts and can significantly contribute to the formation of precipitation. Intensification of such cyclones corresponding to global warming should lead to a transformation of typical CDF, as modern outliers will become regular components of the Pareto law. This change in the statistics of extreme events reflects the nonstationarity of the climate state. The influence of polar lows on the formation of large daily precipitation amounts is not felt.

In the study of diagnosing climate simulations and understanding the dynamics of precipitation extremes, it is an essential step to adopt a simple model to relate water vapor condensation and precipitation, which occur at cloud-microphysical and convective scales, to large-scale variables. Several simple models have been proposed; however, improvement is still needed in both their accuracy and/or the physical basis. Here, we propose a two-plume convective model that takes into account the subgrid inhomogeneity of precipitation extremes. The convective model has three components, i.e., cloud condensation, rain evaporation, and environmental descent, and is built upon the zero-buoyancy approximation and guidance from the high-resolution reanalysis. Evaluated against the CMIP5 climate simulations, the convective model shows large improvements in reproducing precipitation extremes compared to previously proposed models. Thus, the two-plume convective model better captures the main physical processes and serves as a useful diagnostic tool for precipitation extremes. [ABSTRACT FROM AUTHOR]

We develop new flexible univariate models for light‐tailed and heavy‐tailed data, which extend a hierarchical representation of the generalized Pareto (GP) limit for threshold exceedances. These models can accommodate departure from asymptotic threshold stability in finite samples while keeping the asymptotic GP distribution as a special (or boundary) case and can capture the tails and the bulk jointly without losing much flexibility. Spatial dependence is modeled through a latent process, while the data are assumed to be conditionally independent. Focusing on a gamma–gamma model construction, we design penalized complexity priors for crucial model parameters, shrinking our proposed spatial Bayesian hierarchical model toward a simpler reference whose marginal distributions are GP with moderately heavy tails. Our model can be fitted in fairly high dimensions using Markov chain Monte Carlo by exploiting the Metropolis‐adjusted Langevin algorithm (MALA), which guarantees fast convergence of Markov chains with efficient block proposals for the latent variables. We also develop an adaptive scheme to calibrate the MALA tuning parameters. Moreover, our model avoids the expensive numerical evaluations of multifold integrals in censored likelihood expressions. We demonstrate our new methodology by simulation and application to a dataset of extreme rainfall events that occurred in Germany. Our fitted gamma–gamma model provides a satisfactory performance and can be successfully used to predict rainfall extremes at unobserved locations. [ABSTRACT FROM AUTHOR]

The simultaneous occurrence of extremely wet winters at multiple locations in the same region can contribute to widespread flooding and associated socio‐economic losses. However, the spatial extent of precipitation extremes (i.e., the area in which nearby locations experience precipitation extremes simultaneously) and its future changes are largely overlooked in climate assessments. Employing new multi‐thousand‐year climate model simulations, we show that under both 2.0 °C and 1.5 °C warming scenarios, wintertime total precipitation extreme extents would increase over about 80%–90% of the Northern Hemisphere extratropics (i.e., of the latitude band 28°–78°N). Stabilizing at 1.5 °C rather than 2.0 °C would reduce the average magnitude of the increase by 1.7–2 times. According to the climate model, the increased extents are caused by increases in precipitation intensity rather than changes in the spatial organization of the events. Relatively small percentage increases in precipitation intensities (e.g., by 4%) can drive disproportionately larger, by 1–2 orders of magnitude, growth in the spatial extents (by 93%). Plain Language Summary: One of the most impact‐relevant and studied effects of global warming is the intensification of precipitation extremes. When extremely wet winters occur simultaneously at multiple locations within the same region, their cumulative impacts can be particularly high and enhanced as a result of limited resources available to cope with simultaneous damages. Despite the impacts caused by widespread extremes, climate change studies have typically disregarded the spatial extension of the extremes. Here, based on new multi‐thousand‐year climate model simulations, we show that—over most of the Northern Hemisphere extratropics, that is, most of the latitude band 28°–78°N—the spatial footprint of total wintertime precipitation extremes is projected to largely widen in the future. This widening results from a warmer, and therefore moister, atmosphere that will intensify precipitation. Holding global warming to 1.5 °C rather than 2.0 °C, in line with the Paris Agreement, would be beneficial to society as it could limit the average increase in the extension over the Northern Hemisphere extratropics by up to a factor of 2. To develop better preparedness for such extreme events, stakeholders should consider that a small increase in precipitation intensities (for example, by 4%) could result in large (by 93%) increases in spatial extent. Key Points: The spatial extent of wintertime total precipitation extremes is projected to increase over most of the Northern Hemisphere in the futureChanges in the spatial organization, that is, dependence, of precipitation extremes only marginally affect the spatial extentsIncreased precipitation magnitudes can cause a disproportionate (even 20–90 times larger) increase in the precipitation extreme extents [ABSTRACT FROM AUTHOR]

Extreme precipitation has increased in frequency and intensity across the Conterminous U.S. (CONUS). This trend is expected to continue under future climate change. The cause is a combination of thermodynamic (i.e., warmer temperatures increase the atmospheric moisture content) and dynamic changes (e.g., shifts in cyclone frequency and tracks). It is well‐established that thermodynamic changes will intensify extreme precipitation events, but the impacts of dynamic changes are more uncertain. Extreme events are, per definition, rare and occur in unusual weather situations that are distinctly different from regular day‐to‐day weather. We take advantage of this and identify extreme precipitation‐producing weather patterns (XWTs) for all major watersheds across the CONUS by using a novel algorithm. We show that a set of one to four XWTs per watershed are causing extreme precipitation accumulations. These XWTs can be detected based on their synoptic‐scale fingerprint and are associated with West Coast atmospheric rivers, troughing in the desert Southwest, cutoff lows and troughs in the central and northwestern plains, and tropical cyclones along the Gulf and Atlantic coast. The algorithm is flexible enough to provide reliable results for city to major watershed‐scales and can detect extremes that are unprecedented in the training record. Importantly, this approach allows us to assess long‐term trends in extreme precipitation dynamics and reveal that XWT frequencies increased significantly in most U.S. watersheds during the 20th century indicating that changes in the atmospheric dynamics played an important role in historic extreme precipitation increases. Key Points: Archetypal extreme precipitation weather types (XWTs) are identified with a novel algorithmXWTs allow detecting extreme precipitation days in watersheds according to their synoptic‐scale patternsXWT frequency increases indicate a strong dynamic contribution to raising U.S. extreme precipitation [ABSTRACT FROM AUTHOR]