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ABSTRACT This study assesses the performance of 28 NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX‐GDDP‐CMIP6) models and their multi‐model ensemble (MME) in simulating mean and extreme precipitation across sub‐Saharan Africa from 1985 to 2014. The Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) are used as reference datasets. Various statistical metrics such as the mean bias (MB), spatial correlation coefficients (SCCs), Taylor skill scores (TSS) and comprehensive ranking index (CRI) are employed to evaluate the performance of NEX‐GDDP‐CMIP6 models at both annual and seasonal scales. Results show that the NEX‐GDDP‐CMIP6 can reproduce the observed annual precipitation cycle in all the subregions, with the model spread within observational uncertainties. The MME also successfully reproduces the spatial distribution of mean precipitation, achieving SCCs and TSSs greater than 0.8 across all subregions. The biases in mean precipitation are consistent across different reference datasets. However, most of the NEX‐GDDP‐CMIP6 models show trends of mean precipitation opposite to observations. While the MME can generally reproduce the spatial distribution of extreme precipitation, its performance varies with the reference dataset, particularly for the number of rainy days (RR1) and maximum consecutive dry days (CDD). TSS values for extreme precipitation indices differ significantly by region, reference data and index, with the lowest values over South Central Africa and the highest over West Southern Africa. The CRI indicates that no single model consistently outperforms others across all subregions, even within the same region, when compared to both MSWEP and CHIRPS. These results may be helpful when using NEX‐GDDP‐CMIP6 models for future projections and impact assessment studies in sub‐Saharan Africa. [ABSTRACT FROM AUTHOR]

Climate change is leading to alterations in the dynamic and thermodynamic climate systems worldwide, including the Indian summer monsoon (ISM), which supports more than a billion population and drives the Indian economy. The anthropogenic climate change induces unprecedented transformations in the natural and ecological systems, such as the increased probability of precipitation extremes, changes in their frequency, duration and spatial variabilities. This current study aims to project the regional landscape‐based metric, velocity of climate change (VoCC) and associated climatic exposure regarding precipitation extremes (PEs) for India and its different biogeographic zones. The climate velocities of mean precipitation, 95th, 99.5th and 99.9th percentiles of precipitation for the ISM season are presented for the historical and three projected time slices under the RCP8.5 scenario. ROM, a state‐of‐the‐art regional earth system model over the CORDEX‐South Asia domain, was used in the study. It was observed that the intense and very intense rainfall (95th, 99.5th and 99.9th percentiles) was enhanced over most of the study region in the near‐ and mid‐future compared to the far‐future. The intense rainfall exhibited higher climate velocity than the mean and very intense precipitation in the near‐future. The southern part of the Indian subcontinent usually displayed positive VoCC values for the historical and near‐future time slices compared to the northern part of the Indian peninsula, particularly the intense and very intense precipitation. The climatic exposure for all‐India was also higher in the near‐ and mid‐future compared to the far‐future, especially for the intense rainfall followed by the mean and very intense rainfall. These results suggest the need for focusing the adaptation and mitigation measures towards managing the near‐term impacts of PEs in relation to the long‐term impacts, especially on the country's diverse flora. [ABSTRACT FROM AUTHOR]

Assessing the impacts of anthropogenic land use and land cover change (LULCC) on climate extremes is crucial for the public. This necessitates the utilization of state-of-the-art experiments and datasets to enhance our understanding. Here, we used CMIP6-LUMIP experimental results to show the biogeophysical effects of LULCC on extreme temperature and precipitation during the historical period. Three regions with intense LULCC in the Northern Hemisphere experienced cooling effects caused by LULCC. In contrast, only the highest maximum temperature (TXx) showed slight warming effects in three regions with intense LULCC in the Southern Hemisphere near the tropics. The maximum changes in intensity were an ∼ 0.8 °C decrease in the TXx and nearly a 2 °C decrease in the 90th percentile in the lowest minimum temperature (TNn) in North America. South Asia experienced ∼ 4% and 10-day decreases in warm days (TX90p) and warm spell duration (WSDI), respectively, in the 90th percentile. The precipitation response to LULCC displayed a clear difference between the Northern Hemisphere (wetter) and the Southern Hemisphere (drier), especially in terms of mean precipitation and wet days (R1mm). Previous studies support our findings that the impacts of LULCC on temperature extremes are greater in regions with intense LULCC than in remote areas. Seasonal results showed that March-April-May (MAM) and June-July-August (JJA) contributed more to the maximum temperature changes, while the minimum temperature experienced greater responses in MAM and December-January-February (DJF). LULCC tended to induce a shift toward either warmer and drier conditions or wetter and colder conditions in approximately two-thirds of season–region pairs. [ABSTRACT FROM AUTHOR]

Mountain regions are recognised as hot-spots of climate change. Although the existence of an Elevation-Dependent Warming has been extensively confirmed in several mountain areas of the globe, fewer studies have analysed the elevational stratification of temporal trends of other climate variables, and particularly for precipitation. This study analyses changes in mean precipitation and its extremes in ERA5 global reanalysis data in key mountain areas of the globe, along with their elevational dependence, from 1951 to 2020. These include the Tibetan Plateau, the US Rocky Mountains, the Greater Alpine Region, and the Andes, as representative of different latitudes and climatic influences. Our analysis reveals common patterns of elevational dependent change in precipitation and its extremes in most of the mountainous areas, which emerge beyond their geographical differences. A positive elevational gradient of trends of extreme precipitation indices is found in the Tibetan Plateau, the Greater Alpine Region, and the subtropical Andes, highlighting a wetting effect (positive trends) at very high elevations. In contrast, the Rocky Mountains exhibit a negative elevational gradient, with a drying effect (negative trends) increasing with the elevation. Notably, a simple linear regression proved to be effective to describe the stratification of change in the Greater Alpine Region and the Rocky Mountains, whereas more complex vertical patterns need to be considered for the Andes and the Tibetan Plateau. Mean precipitation, heavy (≥ 10 mm) precipitation and the length of consecutive wet days show a consistent elevation-dependent stratification within each of the study areas, suggesting possible common driving mechanisms. [ABSTRACT FROM AUTHOR]

The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario. [ABSTRACT FROM AUTHOR]

Due to global warming, precipitation extremes are becoming more frequent and more severe, further exacerbating the uneven distribution of daily precipitation. In this study, we explored how many days in a year it takes to get to a certain amount of the precipitation that falls annually. We analyzed daily precipitation from gridded observations across the European continent and found that it generally took 22 to 34 of the wettest days to contribute to 50% of yearly totals (WD50). We found various degrees of alignment between gridded observations and ground measurements. Building on this, we examined changes in WD50 and detected widespread shifts toward fewer days in WD50 between the periods of 1950–1985 and 1986–2021. In addition, about one quarter of the European land also exhibited significant, decreasing trends in WD50 over the last 7 decades. Overall, this work showed an intensification of annual precipitation regimes. Plain Language Summary: In a changing climate, the European continent is getting heavier rainfall events and longer, more frequent dryspells. As such, the wettest days in a year make up for more and more of the total precipitation that falls annually. In this study, we analyzed various precipitation records and determined that throughout most parts of Europe, 50% of annual precipitation falls over just 22 to 34 of the wettest days. We examined potential changes over the period of 1950–2021, and found that in many European countries, it takes fewer and fewer days to reach 50% of annual precipitation. With precipitation regimes getting faster‐paced, water availability could become further stressed across the agricultural, hydropower, ecological, and urban sectors. Key Points: Large floods are caused by peak rainfall events, which contribute disproportionately to annual precipitation as measured by the number of Wettest Days to 50% of yearly totals (WD50)WD50 was calculated for gridded observations to capture heterogeneous patterns across Europe in support of water management (e.g., Sustainable Development Goal #6)We also detected robust changes toward fewer days in WD50 between 1950–1985 and 1986–2021, indicating an intensification of hydrological processes in Europe [ABSTRACT FROM AUTHOR]

The frequency and intensity of extreme precipitation events are on the rise worldwide. Despite extensive efforts, regional climate models still show significant biases for extreme precipitation events, often due to factors like improper physics, the choice of land surface model, and spatial domain. Thus, this study uses a Coupled Land–Atmosphere Regional Climate Model version 4.7 (RegCM4.7) to explore how the choice of land surface models (LSMs) and domain extent affects the simulation of extreme precipitation over India. In this regard, a total of four sensitivity experiments have been carried out using two LSMs (CLM4.5 and BATS) over each of the two domains (one over the bigger South Asia CORDEX domain and another for the smaller domain over the Indian region). The main objective is to provide a holistic idea for obtaining an optimum model domain and LSMs for precipitation extremes over India. The model performance is demonstrated for extreme precipitation and associated processes. The result shows the systematic discrepancy in simulating extreme precipitation with a strong inter-simulation spread, indicating the strong sensitivity of extreme precipitation on the LSMs as well as the model domain. The BATS configuration shows a significant overestimation of consecutive wet days and very low precipitation, partially associated with a deficiency in convection. By contrast, the considerable underestimation of intense precipitation can be attributed to the presence of frequent, light drizzle, which hinders the accumulation of moisture in the atmosphere to a sufficient degree to prevent extreme rainfall. Despite significant improvement, the best-configured model (CLM with Indian domain) still indicates substantial bias for extreme precipitation. This deficiency in the model could potentially be mitigated by enhancing both horizontal and vertical resolutions. Nevertheless, further research is needed to explore other physics parameterizations and dynamic mechanisms to address this issue. [ABSTRACT FROM AUTHOR]

Precipitation is a meteorological variable that plays a vital role in determining water resources and managing water risks. The intensity and frequency of precipitation extremes have increased in recent years; however, the changes in precipitation regimes across Pakistan remain unclear. This study analysed spatial and temporal changes in the precipitation concentration index (PCI) and concentration index (CI) from 100 meteorological stations across Pakistan from 1981 to 2017. Using innovative trend analysis (ITA) and Pettitt's test, we detected trends and change points in the annual and wet season PCI and CI. Additionally, we examined the relationship between PCI/CI values and latitude and altitude. The results revealed that the CI and PCI ranged between 0.48–0.78 and 10–55, respectively, with higher values being more prevalent in the dry and hot zones of the country. The study established a linear relationship between PCI/CI values, latitude and altitude, with higher values at lower altitudes and a decrease in values from south to north. Most stations presented a significant positive ITA trend for PCI and CI from 1981 to 2017, with a change between 1985 and 1995, indicating an overall increase in heavy precipitation events in the area. These findings are crucial for disaster preparedness, water resource planning, the mitigation of drought and flood risks, and conservation efforts. [ABSTRACT FROM AUTHOR]

Extreme weather events are more detrimental to human culture and ecosystems than typical weather patterns. A multimodel ensemble (MME) of the top-performing global climate models (GCMs) to simulate 11 precipitation extremes was selected using a hybrid method to project their changes in Pakistan. It also compared the benefits of using all GCMs compared to using only selected GCMs when projecting precipitation extremes for two future periods (2020–2059) and (2060–2099) for four shared socioeconomic pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Results showed that EC-Earth3-Veg, MRI-ESM2-0 and NorESM2-MM performed best among GCMs in simulating historical and projecting future precipitation extremes. Compared to the MME of all GCMs, the uncertainty in future projections of all precipitation indices of the selected GCMs were significantly smaller. The MME median of the selected GCMs showed increased precipitation extremes over most of Pakistan. The greater increases were in RX1day by 6–12 mm, RX5day by 12–20 mm, Prcptot by 40–50 mm, R95ptot by greater than 30 mm, R99ptot by more than 9 mm, R4mm ≥ 4 mm by 0–4 days, R10mm by 2–6 days, R20mm by 1–3 days, and SDII by 1 mm/day, CWD by one day, CDD by 0–4 days in the northern high elevated areas for SSP5-8.5 in the late future. These results emphasize the greater influence of climate change on precipitation extremes in the northern, high-elevation areas, which provide the majority of the country's water. This emphasizes the necessity to adopt suitable climate change mitigation strategies for sustainable development, particularly in the country's northern regions. [ABSTRACT FROM AUTHOR]

Based on data from a period of 72 years (1951-2022), the impact of atmospheric circulation on precipitation, which is usually the source of floods, was assessed for the area of the Western Carpathians. The analysis was performed for the season from April to October using daily rainfall totals originated from the E-OBS database and the calendar of circulation types according to Niedźwiedź's classification. A very significant influence of cyclonic circulation with the airflow from the north and northeast on the amount of daily rainfall was found. This impact is generally visible throughout the entire area of interest, but the most spectacular in the Tatra region (Tatra Mountains and Low Tatras) and on the Czech-Polish-Slovak border (White Carpathians, Maple Mountains, Silesian Beskid). Simultaneously, the analysis showed quite large spatial variations in rainfall, which results from very complex local conditions, especially the orography. [ABSTRACT FROM AUTHOR]

Squall lines are substantially influenced by the interaction of low‐level shear with cold pools associated with convective downdrafts. Beyond an optimal shear amplitude, squall lines tend to orient themselves at an angle with respect to the low‐level shear. While the mechanisms behind squall line orientation seem to be increasingly well understood, uncertainties remain on the implications of this orientation. Roca and Fiolleau (2020, https://doi.org/10.1038/s43247-020-00015-4) show that long lived mesoscale convective systems, including squall lines, are disproportionately involved in rainfall extremes in the tropics. This article investigates the influence of the interaction between low‐level shear and squall line outflow on squall line generated precipitation extrema in the tropics. Using a cloud resolving model, simulated squall lines in radiative convective equilibrium amid a shear‐dominated regime (super optimal), a balanced regime (optimal), and an outflow dominated regime (suboptimal). Our results show that precipitation extremes in squall lines are 40% more intense in the case of optimal shear and remain 30% superior in the superoptimal regime relative to a disorganized case. With a theoretical scaling of precipitation extremes (C. Muller & Takayabu, 2020, https://doi.org/10.1088/1748-9326/ab7130), we show that the condensation rates control the amplification of precipitation extremes in tropical squall lines, mainly due to its change in vertical mass flux (dynamic component). The reduction of dilution by entrainment explains half of this change, consistent with Mulholland et al. (2021, https://doi.org/10.1175/jas-d-20-0299.1). The other half is explained by increased cloud‐base velocity intensity in optimal and superoptimal squall lines. Plain Language Summary: Squall lines are bands of clouds and thunderstorms spanning hundreds of kilometers, also called quasi‐linear mesoscale convective systems. These systems are associated with extreme weather conditions, including extreme rainfall rates. To better understand and therefore predict this high impact phenomenon, this study investigates the physical processes leading to enhanced precipitation rates when clouds are organized into squall lines, using idealized high‐resolution simulations. Interestingly, the dynamics of squall lines, notably their wind structures, are found to play a key role in setting the intensity of extreme rainfall rates. Key Points: Precipitation extremes are enhanced by about 30%–40% in optimal and superoptimal squall lines compared to random convectionThe enhancement of extremes is due to reduced dilution by entrainment and enhanced initial vertical velocity of updrafts in optimal and superoptimal regimesThe enhanced vertical velocity in convective updrafts does not depend on the orientation of squall lines in the superoptimal regime [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) curves are commonly used in engineering practice for the hydraulic design of flood protection infrastructures and flood risk management. IDF curves are simple functions between the rainfall intensity, the timescale at which the rainfall process is studied, and the return period. This work proposes and tests a new methodological framework for the spatial analysis of extreme rainfall depth at different timescales, taking advantage of two different precipitation datasets: local observational and gridded products. On the one hand, the proposed method overcomes or reduces known issues related to observational datasets (missing data and short temporal coverage, outliers, systematic biases and inhomogeneities, etc.). On the other hand, it allows incorporating appropriately terrain dependencies on the spatial distribution of the extreme precipitation regime. Finally, it allows to estimate the IDF curves at regional level overcoming the deficiencies of the classical regional approaches commonly used in practice. The method has been tested to compute IDF curves all over the Basque Country, contrasting results with respect to local analyses. Results show the method robustness against outliers, missing data, systematic biases and short length time series. Moreover, since generalized extreme value (GEV)-parameters from daily gridded dataset are used as covariates, the proposed approach allows coherent spatial interpolation/extrapolation of IDF curves properly accounting for the influence of orographic factors. In addition, due to the current coexistence of local observations and gridded datasets at regional (e.g. The Alps), national (e.g. Spain, France, etc.) or international scale (e.g. E-OBS for Europe or Daymet for the United States of America), the proposed methodology has a wide range of applicability in order to fulfill the known gaps of the observational datasets and reduce the uncertainty related to analysis and characterization of the extreme precipitation regime. [ABSTRACT FROM AUTHOR]

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). If a significant correlation exists between the predictand and the predictor, observations of the latter can be used to constrain model projections of the former and to narrow their uncertainties. In the present study, the EC technique has been applied to the analysis of precipitation, one of the variables most affected by model uncertainties and still insufficiently analysed in the context of ECs, particularly for the recent CMIP6 model ensemble. The main challenge in determining an EC is establishing if the relationship found is physically meaningful and robust to the composition of the model ensemble. Four precipitation ECs already documented in the literature and so far tested only with CMIP3/CMIP5, three of them involving the analysis of extreme precipitation, have been reconsidered in this paper. Their existence and robustness are evaluated using different subsets of CMIP5 and CMIP6 models, verifying if the EC is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Most ECs considered do not pass this test: we found one EC not to be robust in both CMIP5 and CMIP6, other two exist and are robust in CMIP5 but not in CMIP6, and only one is verified and is robust in both model ensembles. [ABSTRACT FROM AUTHOR]

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at "convection-permitting" resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling. [ABSTRACT FROM AUTHOR]

Precipitation extremes are a major impact-relevant implication of climate change. Rising temperatures increase the moisture holding capacity at a rate of ≈ 7 % K - 1 , called the Clausius-Claypeyron (CC) scaling, which can lead to intense precipitation which last for short duration. At a regional level, the scaling of extremes deviates from this expected scaling rate. Large scale circulation dynamics and local variability in thermodynamic influences are suspected to cause these deviation, but these drivers differ across seasons. In the present study, we use ERA5 reanalysis to evaluate the seasonal changes in precipitation-temperature scaling rates over the Indian subcontinent. We further determine the deviations from the expected CC scaling rate, and the precipitation extremes are decomposed to their dynamic and thermodynamic contribution across different seasons. It is found that significant seasonal contrast exists in the dynamic and thermodynamic contributions, with the latter dominating during the Indian summer monsoon season, while the former being higher during the pre-monsoon and post-monsoon season. Further analysis highlights that the lower dynamic contribution is attributed to drop in dew point temperatures and Convective Available Potential Energy during extremes. The primary drivers causing the extremes in different seasons are also pointed out, further improving the understanding of how the intensity and frequency of precipitation extremes changes spatially across different seasons, and what are the physical drivers causing these changes. [ABSTRACT FROM AUTHOR]

A regional earth system model (ROM) was used to examine the projected change in the precipitation extremes (PEs) and associated dynamical and thermodynamical processes over India during the Indian summer monsoon (ISM). In this regard, PEs are computed for India, its six homogeneous regions and subregions the Western Ghats (WG) and central India (CI). The changes are computed for mid‐future (2040–2069: MDF) and far‐future (2070–2099: FRF) with respect to the historical period (1969–2000). ROM showed better resemblance with observation in simulating PEs over other regional climate models (RCMs) that participated in the CORDEX‐CORE simulation. The intense rainfall (95th percentile: R95) is expected to be enhanced over most of the region during MDF that further intensifies in FRF except CNE (central northeast) and NEI (northeast India), where the increase in R95 was restricted only up to MDF. Interestingly, very intense rainfall (99.9th percentile: R99) showed robust increases in both MDF and FRF for all regions. Additionally, long wet (dry) events were shortened (lengthen). Moreover, the short wet and dry spell frequency has increased, while the duration of the wet (dry) spell has decreased (increased) in both time slices over India with noticeable regional variation. This is attributed to the strong cyclonic circulation, reduced vertical wind shear and enhanced moisture transport during the ISM in both time slices. It is very important to highlight the substantial changes in the precipitation extremes to have better planning and strategies that will be helpful to minimize the incurred losses. [ABSTRACT FROM AUTHOR]

Accounting for the diversity in El Niño Southern Oscillation (ENSO)'s spatial pattern, with the novel ENSO longitudinal index (ELI), we evaluate the simulation of its teleconnections to US winter precipitation extremes by seven global high‐resolution (HR) Earth System Models (ESM). Six (four) HR ESMs simulate the observed increase in precipitation extremes over Southwest US (Southeast US) during ELI‐defined El Niño events better than their low‐resolution counterparts, which are low‐biased. The stronger ENSO‐dependence over the Southwest US and Southeast US in those models is associated with an improved simulation of moisture flux into the regions and/or storm track activity there. HR ESMs, however, generally overestimate the increase in precipitation extremes over the Pacific‐Northwest during La Niña events. Model bias there is associated with bias in moisture transport into the region during La Niña events, which is amplified by the enhanced vertical mass fluxes in HR. Plain Language Summary: El Niño Southern Oscillation (ENSO) comes in many flavors with diverse spatial pattern of ocean warming and associated heavy tropical rainfall over the deep tropical Pacific. This ENSO diversity is one of the major reasons behind our limited understanding and prediction of its global impacts, particularly on climate extremes. A recent approach to characterize the full spectrum of ENSO using a simple index shows promise in improving our understanding. Here, we use this new index to evaluate if new state‐of‐the‐art high resolution Earth System Models can capture the remote impacts of ENSO on US precipitation extremes in the winter. We find that some of these models can credibly simulate these ENSO teleconnections, across its many flavors, over the SE‐US and SW‐US, and generally improve upon their low resolution model counterparts. Key Points: We evaluate seven high‐resolution (HR) Earth System Models' simulation of diverse El Niño Southern Oscillation (ENSO) teleconnections to US winter precipitation extremesSimulation of ENSO‐dependent Southwest‐US (Southeast‐US) precipitation extremes is improved in six (four) models with resolution increaseThis is due to improvements in ENSO dependent moisture transport, storm track activity, and vertical mass fluxes in HR models [ABSTRACT FROM AUTHOR]

This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The standardized precipitation index (SPI) is used to represent drought on a 3-month scale. We analyse various drought characteristics (duration, events, frequency, intensity, and severity) for all drought months, and moderate, severe, and extreme drought conditions. The results show that drought occurs across the continent, with the equatorial regions displaying more negative SPI values, especially for moderate and severe droughts. On the other hand, Eastern Sahara and Western Southern Africa portray less negative SPI values. The study also reveals that extreme drought months have the largest interannual variability, followed by all drought months and severe drought months. The trend analysis of SPI shows a significantly increasing trend in drought episodes over most regions of Africa, especially tropical areas. Drought characteristics vary greatly across different regions of Africa, with some areas experiencing longer and more severe droughts than others. The equatorial region has the highest number of drought events, with longer durations for severe and extreme drought months. The Eastern Sahara region has a low number of drought events but with longer durations for moderate, severe, and extreme drought months, leading to an overall higher drought severity over the area. In contrast, Western Southern Africa and Madagascar display a consistently low drought severity for all categories. The study demonstrates the importance of conducting drought analysis for different drought levels instead of using all drought months. Drought management and adaptation strategies need to enhance community resilience to changing drought situations and consider drought variability in order to mitigate different impacts of drought across the continent. [ABSTRACT FROM AUTHOR]

As precipitation (PPT) regimes are becoming more extreme worldwide, the inter‐annual variation in PPT amount at a given location is becoming greater than ever before. Yet, our understanding of grassland responses to extreme drought and wet years, as well as the pattern of the relationship between PPT and above‐ground net primary productivity (ANPP) along a PPT gradient from extreme drought to extreme wetness scenarios, remains unclear, hampering accurate forecasting of the grassland feedbacks to future PPT changes.We addressed this research gap by a 4‐year, multi‐level PPT gradient experiment that included PPT amounts simulating extremely dry and wet years in combination with a long‐term (40 years) observational study in a grassland ecosystem.We found that the ANPP reductions due to extreme droughts were greater than the magnitude of positive responses to extreme wet years. The PPT‐ANPP relationship along the gradient of PPT followed a negative asymmetric (concave‐down) pattern. We identified the major mechanisms for this pattern were a shift in primary limiting resource for ANPP from water to nitrogen with increasing PPT and different responses of the plant functional groups presented in the community. With the increase of PPT from extremely low to extremely high levels, the relative abundance of forbs increased, while that of grasses decreased and annuals was not significantly affected.This study illustrates how a grassland responded to PPT extremes and highlights the role of shifts in resource limitation in affecting the PPT‐ANPP relationship. The negative asymmetric pattern suggests that the reduction of ANPP in extremely drought years cannot be mitigated by its positive response to extremely wet years. Thus, to accurately forecast future ecosystem feedback to climate change, we should take into account the negative asymmetric feature of the ecosystem response to stronger climate variation, as well as recognize that the resource limitations will be fundamentally altered by increasing frequency and intensity of climate extremes in the future. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Accurate knowledge on spatiotemporal characteristics of historical precipitation extremes could provide great potential guidance for preventing hydrological‐related disasters caused by precipitation extremes in the future. On the basis of the fifth generation of atmospheric reanalysis precipitation data by the European Centre for Medium Range Weather Forecasts (ERA5, 0.25°, 1 hourly, 1950–2020) with high spatiotemporal resolutions, continuity and quality, this study analyzed the spatiotemporal characteristics of precipitation extremes over the Ganjiang River Basin and its surroundings during 1950–2020. The main conclusions include, but are not limited to, the following: (1) In general, precipitation extremes present increasing trends over most areas of the basin and its surroundings. For instance, areas showing upward trends of R10, SDII and PRCPTOT account for ~93.45%, ~66.36%, and ~88.18%, respectively. (2) The spatiotemporal variations of precipitation extremes over the Ganjiang River Basin and its surroundings show obvious northwest–southeast differences. For instance, precipitation extremes are increasing in the southeastern parts, but they are decreasing in the northwestern parts. (3) High‐value clusters are also identified in the southeast (e.g., R10, SDII, R95P and PRCPTOT, accounting for ~20.71%, ~20.72%, ~25.88%, and ~22.56%, respectively) and low‐value clusters in the northwest (e.g., Rx5day, SDII and R95P, accounting for ~18.05%, ~27.03%, and ~21.18%, respectively). (4) The spatiotemporal variations of precipitation extremes in both the southeast and northwest are quite stable. For example, regions with less than five abrupt change points of R10, SDII, and PRCPTOT account for 77.49%, 54.84%, and 81.74%, respectively. [ABSTRACT FROM AUTHOR]

The relationship between boreal summer intraseasonal oscillation (BSISO) and precipitation extremes over Indonesia is investigated using observational datasets from 30 years (1987–2016) of rain gauge measurements and the gridded Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation of Water Resources from 1998 to 2015. The results indicate that the frequency of daily extreme precipitation events in Indonesia (defined as total precipitation above the 95th percentile) during extended boreal summer (May–August) is significantly modulated by BSISO, especially over the western and northern regions. Under the influences of BSISO1, the probability of the precipitation extremes over Sumatra and Borneo increases by 20–120% (relative to the seasonal probability) during phases 1–3 and approximately 50–80% over the eastern part of Borneo and Sulawesi during phase 4. Under BSISO2, the probability of the extremes increases up to 40% over Sumatra during phases 1 and 2 and up to 140% over Borneo and Sulawesi during phases 2 and 3. The increase in the probability of extreme summer precipitation is associated with enhanced large‐scale moisture flux convergence and upward moisture transport induced by the active phases of BSISO. These results provide potential information for medium‐ to extended‐range predictions of summer precipitation extremes in Indonesia. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are filamentary regions of high-water vapour flux in the lower troposphere that contribute significantly to poleward moisture movement in mid-latitude regions. Key characteristics (frequency, duration, and intensity) of ARs have been explored to recognize the regions vulnerable to AR-flood. To investigate the association of ARs with large-scale climate oscillations (LSCOs), precipitation extremes (PEs) maximum 1-day precipitation (Rx1day), maximum consecutive 5-day precipitation (Rx5day), precipitation amount from very wet days (R95pTOT) are explored in a non-stationary framework of generalized extreme value distribution, taking the Arctic Oscillation, North Atlantic Oscillation, El Niño Southern Oscillation, and Pacific Decadal Oscillation (PDO) as covariates. In almost 30% of regions around the globe, May-June-July-August-September (MJJAS) season PDO was found to be the relatively most influential covariate for capturing PEs. The west coast of North America and of Europe, southernmost South America, central East Asia, New Zealand, and Australia have been identified as the most critical regions associated with AR linked with PE-associated LSCOs. [ABSTRACT FROM AUTHOR]

The relationship between boreal summer intraseasonal oscillation (BSISO) and precipitation extremes over Indonesia is investigated using observational datasets from 30 years (1987–2016) of rain gauge measurements and the gridded Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation of Water Resources from 1998 to 2015. The results indicate that the frequency of daily extreme precipitation events in Indonesia (defined as total precipitation above the 95th percentile) during extended boreal summer (May–August) is significantly modulated by BSISO, especially over the western and northern regions. Under the influences of BSISO1, the probability of the precipitation extremes over Sumatra and Borneo increases by 20–120% (relative to the seasonal probability) during phases 1–3 and approximately 50–80% over the eastern part of Borneo and Sulawesi during phase 4. Under BSISO2, the probability of the extremes increases up to 40% over Sumatra during phases 1 and 2 and up to 140% over Borneo and Sulawesi during phases 2 and 3. The increase in the probability of extreme summer precipitation is associated with enhanced large‐scale moisture flux convergence and upward moisture transport induced by the active phases of BSISO. These results provide potential information for medium‐ to extended‐range predictions of summer precipitation extremes in Indonesia. [ABSTRACT FROM AUTHOR]

The Regional Atmospheric Modelling System (RAMS) driven by data from the CMIP5 Earth System Model HadGEM2‐ES was used to simulate daily precipitation over the tropical Americas for both current and future climate, including distinct scenarios (representative concentration pathways 4.5 and 8.5) and different time horizons (short‐term, mid‐term and long‐term changes). The major objective was to evaluate possible future changes in extreme events, with emphasis on the intensity of precipitation events and the duration of wet and dry spells. According to RAMS, longer dry spells are expected over most regions of the tropical Americas in the future, with indications for Northeast Brazil, Caribbean, Northern Amazon, and shorter wet spells over Central America and Amazon. With the exception of the Caribbean, there is a general tendency towards the increased frequency of intense precipitation in the tropical Americas. [ABSTRACT FROM AUTHOR]

Warming temperatures are increasing rainfall extremes, yet arthropod responses to climatic fluctuations remain poorly understood. Here, we used spatiotemporal variation in tropical montane climate as a natural experiment to compare the importance of biotic versus abiotic drivers in regulating arthropod biomass. We combined intensive field data on arthropods, leaf phenology and in situ weather across a 1700–3100 m elevation and rainfall gradient, along with desiccation‐resistance experiments and multi‐decadal modelling. We found limited support for biotic drivers with weak increases in some herbivorous taxa on shrubs with new leaves, but no landscape‐scale effects of leaf phenology, which tracked light and cloud cover. Instead, rainfall explained extensive interannual variability with maximum biomass at intermediate rainfall (130 mm month−1) as both 3 months of high and low rainfall reduced arthropods by half. Based on 50 years of regional rainfall, our dynamic arthropod model predicted shifts in the timing of biomass maxima within cloud forests before plant communities transition to seasonally deciduous dry forests (mean annual rainfall 1000–2500 mm vs. <800 mm). Rainfall magnitude was the primary driver, but during high solar insolation, the 'drying power of air' (VPDmax) reduced biomass within days contributing to drought related to the El Niño‐Southern Oscillation (ENSO). Highlighting risks from drought, experiments demonstrated community‐wide susceptibility to desiccation except for some caterpillars in which melanin‐based coloration appeared to reduce the effects of evaporative drying. Overall, we provide multiple lines of evidence that several months of heavy rain or drought reduce arthropod biomass independently of deep‐rooted plants with the potential to destabilize insectivore food webs. [ABSTRACT FROM AUTHOR]

Precipitation extremes present significant risks to Midwest agriculture, water resources, and natural ecosystems. Recently, there is growing attention to the transitions of precipitation extremes, or shifts between heavy precipitation and drought, due to their profound environmental and socio‐economic impacts. Changes in Midwest precipitation extremes and transitions between extremes over the past few decades have been documented; however, their future changes are still unknown. In this study, we estimate the projected changes in transitions of precipitation extremes in the Midwest based on 17 CMIP6 models. Two Standardized Precipitation Index (SPI) based metrics, intra‐annual variability and transitions, are used to quantify the magnitude, duration, and frequency of variability and transitions between wet and dry extremes. Compared with the observation‐based precipitation datasets, the multimodel ensemble median of CMIP6 can reasonably represent the spatial patterns of SPI extremes and transitions. Climate projections show significantly intensified wet extremes across the Midwest by the end of the century, with a greater increase over the northern Midwest and the Great Lakes region. The short‐term SPI also shows intensified dry extremes over the western half of the Midwest. Consequently, there is a significant increase in the magnitude of intra‐annual variability in most areas. Projections also suggest more frequent and rapid transitions between the wet and dry extremes, especially over the Great Lakes region and northern Midwest. Seasonally, more frequent transitions from a wet spring to a dry summer (or from a dry fall to a wet winter/spring) are projected to occur; and generally, the wet and dry conditions between the transitions are projected to be more intense compared to the historical period. Furthermore, the intensified precipitation extremes and accelerated transitions are greatly alleviated under a lower emission scenario, implying that future changes in hydroclimate extremes, and impacts thereof, in the Midwest are sensitive to climate change mitigation. [ABSTRACT FROM AUTHOR]

Global warming has altered the thermodynamic and dynamic environments of climate systems, affecting the biogeochemical processes between the geosphere and atmosphere, which has significant impacts on precipitation extremes and the terrestrial carbon budget of ecosystems. Existing studies have reported a hook structure for precipitation extreme-temperature relationships but have rarely examined the underlying physical mechanisms. Previous studies have also failed to quantify the impact of precipitation on ecosystem productivity, hindering the assessment of future extreme climatic hazards and potential ecosystem risks. To reveal the thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects, this study utilizes over ten multisource datasets (i.e., satellite, reanalysis, climate model, land surface model, machine learning reconstruction, and flux tower measurements). We first assess the response of water-heat-carbon flux to precipitation extremes and explain the underlying physical mechanisms behind the hook structures in terms of atmospheric thermodynamics and dynamics. Based on outputs from five global climate models (GCMs) under ISIMIP3b, we project future changes in the hook structures as well as their impacts on precipitation extremes. Finally, we discuss the impact of precipitation on the terrestrial carbon budget by using outputs from the CLM4.5 model. The results show that precipitation extremes are usually accompanied by strong exchanges of water and heat and demonstrate a nonlinear relationship between precipitation and ecosystem productivity. The intensity (duration) of extreme precipitation is intensifying (decreasing) over most areas of the globe, whereas three-dimensional precipitation events are becoming more concentrated. Atmospheric dynamics play a key role in shaping the hook structure. The structure is not stable; it shifts under climate change and is projected to result in a 10–10% intensification in precipitation by the end of this century. Moderate levels of precipitation contribute to carbon assimilation in ecosystems, and the response of the carbon budget to precipitation is relatively stable under climate change. [ABSTRACT FROM AUTHOR]

The Beijing-Tianjin-Hebei urban agglomeration(BTH) has successively witnessed the extraordinary precipitation extremes (PEs) with huge economic losses and death-toll in the recent decade. To timely and comprehensively understand the PEs in the urban agglomeration, we investigate the characteristic and mechanism of PEs variation based on six extreme precipitation indices (EPIs) including maximum daily precipitation(Rx1day), maximum consecutive 5-day precipitation(Rx5day), total precipitation with daily precipitation more than the 95th percentile (R95P), average daily precipitation on wet days (SDII), heavy precipitation days(R25) and very heavy precipitation days(R50). Our results suggest that the PEs of summertime over the BTH has significantly amplified since 21st century. During 2001–2023, the Rx1day, Rx5day, R95p, SDII, R25 and R50 significantly increased at a rate of 13.5 mm/10a, 26.3 mm/10a, 49.4 mm/10a, 2.2 mm/10a, 0.78d/10a and 0.46d/10a, respectively. The average contribution of urbanization to the increased EPIs is estimated by 21 %. The strengthened East Asian Summer Monsoon, intensified and northward extended West Pacific Subtropical High may increase occurrence and severity of PEs in the era of rapid global warming. Three case studies of PEs in 2012, 2016 and 2023 verify our finding. We hope this study can help policy makers to shape strategies to mitigate or reduce societal impact of PEs under global warming crisis and rapid urbanization. • Precipitation extremes in summer over the Beijing-Tianjin-Hebei urban agglomeration significantly amplified since 21st century. • Urbanization contribution to the increased extreme precipitation indices on average is estimated by 21 %. • The strengthened East Asian Summer Monsoon, intensified and northward extended West Pacific Subtropical High may increase precipitation extremes. [ABSTRACT FROM AUTHOR]

Although climatologically dry, the western slopes of the southern Andes of Peru (WSA) can experience precipitation extremes (PEs) during the summer (December–February) resulting in great economic and human losses. Generally, WSA has a positive upslope gradient in precipitation, meaning more rain falls at higher elevations, but observations have shown this gradient can become negative with higher rainfall near the coastal cities. In this study we analyse 2000–2019 regional atmospheric patterns associated with different upslope precipitation gradients and PEs in WSA using principal component analysis methods and surface station observations. Results show important changes in the atmospheric circulation patterns during the occurrence of PE events. A prevailing pattern of negative southerly wind anomalies and regional warming of the southeastern Pacific Ocean leads to significant increases in moisture along the coast of WSA. Eastern moisture flows associated with the presence of the Bolivian High are observed at upper levels of the atmosphere and transport water vapour from the Amazon to the western side of the Andes. Additionally, there is a blocking effect aloft in response to an intense gradient of geopotential height that attenuates the easterly circulations. These large‐scale mechanisms act to concentrate high precipitable water amounts and high levels of convective available potential energy in the troposphere which favours the vertical velocities essential to trigger PEs. These results increase our knowledge of the large‐scale characteristics of PEs to help with forecasting these impactful events and protecting the more than 1.8 million people living in WSA. [ABSTRACT FROM AUTHOR]

Coarse resolution datasets often poorly capture precipitation properties. It is commonly expected that skill at the local level should increase by refining spatial resolution. Here, we examine the influence of spatial resolution on the accuracy of means and extremes in precipitation by comparing high‐resolution dynamically downscaled data against the driving lower‐resolution reanalysis. We show that the higher‐resolution data are less accurate than lower‐resolution data for both mean and extreme precipitation. The quantitative deterioration (increase in the domain averaged relative error) from coarse to high resolution varies typically between 1%–8% and 5%–30% for mean and extreme precipitation, respectively. We also find that the finer‐scale variability resolved only by the higher‐resolution system and successfully predicted by that system is of negligible magnitude compared to the overall error (less than 1%). We conclude that finer‐scale resolution by itself does not necessarily bring a meaningful improvement in local simulation accuracy. Plain Language Summary: In this study, we examined the impact of spatial resolution on means and extremes in precipitation using high‐resolution dynamically downscaled data against the driving lower‐resolution reanalysis over Australia. We report that high‐resolution data are less accurate than lower resolution data for standard statistics of precipitation. We also discuss the possible reasons for the deterioration and variations between previous studies. The results highlight that increasing the horizontal resolution alone will not help address problems with precipitation. Significant model developments and data assimilation techniques are required to reap the benefits of high horizontal resolution. Key Points: Increasing the horizontal resolution alone did not help address problems with simulating regional precipitation in this case studySignificant model developments are required to reap the benefits of high horizontal resolution [ABSTRACT FROM AUTHOR]

The rise of global surface temperature due to warming climate is expected to increase the intensity and occurrence of extreme precipitation events. Previous studies in Southeast Asia revealed complex variations in changes of precipitation extremes. This study presents a spatial–temporal analysis on changes of precipitation extremes in Peninsular Malaysia by utilizing long‐term daily rainfall records at 64 observed stations during 1989–2018. The modified Mann–Kendall and Sen's slope tests were performed to detect the significance and magnitude of trends in eight extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices. Statistically significant increasing trends are observed for four of these extreme indices in the annual assessment. Spatial analysis demonstrates an obvious contrast between wet and dry regions in patterns of precipitation extremes. Seasonal analysis reveals the intensity and frequency of wet extremes are enhanced significantly during the northeast monsoon season. Significant correlations are found between precipitation extremes and El Niño–Southern Oscillation, particularly in the northern, eastern and southwest regions. Collectively, the evidence presented suggests that the occurrence of precipitation extremes in Peninsular Malaysia tends to be more frequent and intense over the year and is closely associated with the combined effects of tropical monsoon cycles and El Niño–Southern Oscillation. [ABSTRACT FROM AUTHOR]

Central Asia faces an increasing challenge related to water resources arising from severe droughts and floods that have impacted the region in the past decades. Using a comprehensive set of extreme precipitation indices, we assess the performance of CMIP6 models in representing observed precipitation extremes and investigate future responses of these extremes to greenhouse gas emissions under four shared socioeconomic pathways (SSP). Particularly, this study identifies robust signals of projected changes in spring and summer precipitation extremes over central Asia. Results show that the CMIP6 models reasonably reproduced the spatial distribution and variability of precipitation extremes over southern central Asia (SCA) and northern central Asia (NCA) during the spring and summer seasons, respectively. Across the SSPs, the CMIP6 models project a decrease in total spring wet‐day precipitation amount (PRCPTOT) over SCA and a significant increase in PRCPTOT over the NCA. The projected changes are characterized by a significant increase in maximum consecutive 5‐day precipitation (RX5day), wet‐day intensity (SDII), and the number of heavy precipitation days (R10mm); these suggest that the spring will be characterized by intense precipitation extremes. Moreover, no significant change is projected for consecutive wet days (CWD) and dry spells (CDD) over SCA. Nonetheless, the CMIP6 models project a significant increase in summer PRCPTOT over SCA and a decrease in summer PRCPTOT, RX5day, R10mm, SDII, and CWD over NCA while projecting a significant and robust increase in dry spells. It is also found that the severity of the projected dry spell deepens across the SSP spectra and gets more pronounced towards the end of the 21st century. Notably, the intermodel spread of the precipitation extremes is smaller during the spring over SCA, larger during the summer season over NCA, and more probable in the warmer future. [ABSTRACT FROM AUTHOR]

The timing and intensity of precipitation varies from year‐to‐year and is expected to change in the future. Assessing the impacts of this moisture delivery variability on tree growth is important both for future forest health and for our interpretation of pre‐instrumental tree‐ring records. Here, we used the Vaganov‐Shashkin model to investigate how changes in precipitation delivery impact tree growth at five sites representing four species in two North American river basins with high precipitation variability but different seasonal cycles. Evenly distributed precipitation increased tree growth in the Lower Sacramento watershed, while the water‐limited South Platte benefited from concentrated precipitation early in the growing season. Although most experimental simulations retained the pattern of high‐ and low‐growth years, tree growth was reduced with fewer, more intense precipitation events, which could affect interpretation of past climate extremes. Under the RCP4.5 scenario, projected warming offset the potential benefits of increased precipitation on tree growth. Plain Language Summary: Trees are valued for many different reasons, including their ability to reduce atmospheric carbon dioxide levels and to record climate from past centuries. Tree growth in moisture‐limited environments is strongly affected by the amount and timing of moisture the tree receives, as well as by how gradually or abruptly precipitation reaches the tree. The amount, timing, and rate of precipitation almost certainly changed in the past and are likely to be affected by human‐caused warming. In this study, we used a model that "grows" tree cells based on temperature, precipitation, and other environmental data to experimentally change the amount and timing of precipitation and assess how these changes affect tree growth in California and Colorado, USA. We found that although many of these experiments affected the two basins differently, our experiments with increased precipitation concentration and those that used projections of future climate change consistently caused tree growth to decrease. This is important because it could affect our interpretations of tree‐ring records of past climate (since the same amount of precipitation could cause different amounts of growth depending on how it is distributed) and our future tree growth expectations (since fewer, stronger storms and higher temperatures could reduce growth). Key Points: Vaganov‐Shashkin model simulations used to test how variation in timing and intensity of precipitation influences tree growthIdentical precipitation totals with different delivery alters tree growth and could affect interpretation of proxy climate recordsProjected future changes of intensified precipitation and warming temperatures offset growth benefits of increased precipitation totals [ABSTRACT FROM AUTHOR]

The precipitation is classified into tropical cyclone (TC)‐induced precipitation and others, that is, non‐TC precipitation in the Asian continent and the western North Pacific. Traditional frequency analysis for the precipitation, which is a mixture series of annual maximum precipitation generated by TC and non‐TC, does not consider the difference in the generation mechanism with the assumption of independently and identically distributed. To reveal the effect of different generation mechanisms on the frequency analysis of the extreme precipitation, a multiplicative mixture frequency analysis (MMFA) model is adopted to study the interaction of non‐TC maximum precipitation (NTCMP) and TC maximum precipitation (TCMP) to the total precipitation frequency. Applying the NTCMP and TCMP data in Fujian Province located in southeast China, the results indicated that (a) the inconsistencies between NTCMP and TCMP are common, and different inconsistent patterns existed in space; (b) Different characteristics of inconsistency between the two types of precipitation had distinct effect on the estimation results of MMFA model, especially for the high return levels; and (c) The estimation results of MMFA model were demonstrated to be more secure and reliable. Therefore, the MMFA model is a suitable approach to solve the problem of mixture precipitation frequency caused by two generation mechanisms of non‐TC and TC. [ABSTRACT FROM AUTHOR]

This study examines the potential implications of 1.5, 2.0, and 3.0°C global warming levels (GWLs) on the austral summer (November–March) extreme precipitation indices over the Zambezi River basin (ZRB) relative to the control period (1971–2000). We computed extreme precipitation based on daily data from observations and the Coordinated Regional Downscaling Experiment (CORDEX)‐Coordinated Output for Regional Evaluations (CORE) multi‐model ensemble mean (ENSMean). First, we evaluated the performance of the CORDEX‐CORE ENSMean in simulating extreme precipitation based on six indices; the number of rainy days (RR1), simple daily intensity index (SDII), maximum consecutive wet days (CWD), maximum consecutive dry days (CDD), heavy precipitation days (R10), and very heavy precipitation days (R20). The results indicate that the CORDEX‐CORE ENSMean can simulate the spatial distributions of extreme precipitation over the ZRB. However, CORDEX‐CORE largely overestimates the magnitudes of RR1 and CWD. The projected changes show a decrease in RR1, CWD, and R10 under all GWLs, with a robust and pronounced decrease under 3.0°C GWL. In contrast, CDD and SDII are projected to increase under all GWLs, with a robust and pronounced increase in CDD under 3.0°C GWL. The regionally averaged changes show that the median values of CWD, RR1, and R10 (SDII and CDD) are projected to decrease (increase) under all GWLs over the ZRB and sub‐basins. The probability density function (PDF) shows negative (positive) shifts in the mean of CWD, RR1, and R10 (SDII and CDD) over the ZRB and sub‐basins under all GWLs. In contrast, R20 is projected to increase (decrease) over most of the western (eastern) ZRB under all GWLs. Assessing the implications of an additional 0.5 and 1.5°C (1.0°C) warming to 1.5°C (2.0°C) GWL shows that limiting GWL to 1.5°C would restrict the future exposure of the ZRB to extreme precipitation. [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). [ABSTRACT FROM AUTHOR]

Climate change is an essential trigger for mountain disasters (e.g., landslides and debris flow) in South⁃ west China, especially in the Minjiang River Basin. Using the daily observations of 47 meteorological stations, we disclose the spatial temporal variation of precipitation under the background of warming in the Minjiang River Ba⁃ sin during 1979－2018. Then we detect the trend and abrupt change of precipitation extremes, years with precipitation anomaly, and the frequency of drought flood shifts based on a statistical analysis of eleven indexes for precipitation extremes. Moreover, we discuss the impact of climate change on typical events of mountain disasters. Our re⁃ search demonstrates that the temperature generally increased in the Minjiang River Basin over the past four de⁃ cades, particularly in the upper reaches where the prominent landform is mountainous. Furthermore, the spring and winter precipitation raised significantly with the maximum increment in the upstream and the mountainous regions west of the midstream. Meanwhile, the amount and intensity of precipitation extremes increased for mountain stations in the upper reaches, which is related to the more active Southwest Vortex, especially the Jiulong Vortex. In addition, the frequency of drought flood shifts increased remarkably during 1979－2018. Therefore, the mountain disasters were more prone to break out with the magnification of disaster risks due to the combination of significant temperature and precipitation increase in the basin′s upper reaches and the enhanced precipitation extremes in high mountains. [ABSTRACT FROM AUTHOR]

The Himalayan region of Uttarakhand, India, has witnessed floods and landslides, and more extremes are likely in the future. This study examined the projected changes in precipitation extremes by using state-of-the-art, high-resolution (0.25° × 0.25°) statistically downscaled NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) during southwest monsoon season (June to September) under the RCP 4.5 and RCP 8.5 scenarios. The spatial variations of mean precipitation, as well as the extremes obtained from the multi-model mean (MMM) from NEX-GDDP simulations, were compared with Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) observational data for the baseline period (1976–2005). For the future climate, the monsoon precipitation over the Uttarakhand region may increase by ~ 13% and ~ 16% under the RCP 4.5 and RCP 8.5 emission scenarios, respectively, in the near future (2021–2050) and may further increase by 23% and 36% in the far future (2070–2099). The different extreme precipitation indices analyzed show an increase with the exception of consecutive dry days (CDDs) over Uttarakhand, both in the near future and in the far future, under both scenarios. The RCP 4.5 and RCP 8.5 scenarios exhibited a noticeable increase in the highest 1-day rainfall (by 1.4 mm decade−1 and 3.3 mm decade−1) and in the highest 5-day rainfall (by 2.7 mm decade−1 and 7 mm decade−1), along with the extreme R95P precipitation days (by 11% and 22%), and consecutive wet days become more frequent during monsoon season, respectively. The study findings highlight the need for considering more extreme rains in base Himalayan climate resiliency planning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Climate change is one of the major reasons for the increased intensity and frequency of precipitation extremes in tropical regions. This will have a significant impact on underdeveloped countries in South Asia and Southeast Asia. Therefore, this study analyzes changes in spatiotemporal patterns and the joint behavior of precipitation extremes across South Asian and Southeast Asian countries for the historical period (1975 to 2014) and future periods (2021–2060 (F1) and 2061–2100 (F2)). This study develops bias-corrected precipitation data and ranks General Circulation Models (GCMs) using the Empirical Quantile Mapping method and TOPSIS method, respectively. A multi-model ensemble of precipitation extremes is created using the top five GCMs. The Mann Kendall test is used to analyze trends in precipitation extremes. Joint probabilistic analysis is also conducted for different combinations of ETCCDI precipitation-based indices using Archimedes and Elliptical copulas. The results of the trend analysis indicate significant positive trends for R20mm (19.26%), R95pTOT (18.40%), Rx5Day (11.53%), and CWD (10.46%), while CDD (5.07%) shows a significant negative trend across the study area during historical period (1975–2014). The results of R20mm, R95pTOT, and Rx5Day under SSP585 shows almost 19 to 39% of area comes under significant positive trend during the F1 period. This becomes more evident in F2, with trends under SSP585 rising between 32% and 61%. The high-emissions scenario SSP585 reveals an increase in these positive trends compared to SSP126 in both the future periods (F1 and F2). These results indicating a noticeable rise in extreme precipitation events. The analysis of changes in the joint return period of precipitation extremes indicates that the South Peninsular India, North East India, West Central India and Central Northeast India, as well as most parts of Southeast Asian countries, will experience very heavy intensify precipitations in the future. In addition, persistent co-occurrence of dry and wet conditions will be observed in Pakistan, North West India, and Central Northeast India. This study provides useful information on the distribution of precipitation extremes in different regions of the study area. • Projected precipitation extremes are more significant under higher emissions than the lower-emission scenario. • The study quantifies the changes joint return period of precipitation extremes in the South Asia and Southeast Asia. • In the future, the persistent co-occurrence of dry and wet conditions will be observed in Pakistan, NWI and CNI. [ABSTRACT FROM AUTHOR]

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]

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]

Climate change is often described as the average changes in temperature and precipitation; however, it is the change in climate extremes determines the levels of socioeconomic impacts related to climate change. It is generally believed that global warming drives increase in frequency, intensity and duration of precipitation extremes, but these changes vary regionally. Focusing on the relationships between evolution of extreme events and long‐term climate change, here we propose a novel classification scheme based on Kӧppen's system and changes in mean and variability of precipitation, divide the global climate into 20 different changing types and reveal the regional evolution of precipitation extremes. We find that precipitation extremes ascend significantly in wetting regions, especially in tropical and temperate zones, independent with changed variability. As for drying regions, the evolution of extremes is related to precipitation variability. An increase of extremes can still be detected in fluctuant‐drying areas, while a slight decrease can be seen in stabilized‐drying areas. It is surprising to find increase in both wetness and dryness extremes which implies higher intensity of meteorological hazards in densely populated areas. Based on the current and projected growth of population exposure to precipitation extremes, we identify some hotspots with high potential risk in the future. [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]

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 mmday-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]