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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]

For climate risk assessments accurate gridded data sets are needed. An important aspect of such data sets is that they reliably represent the spatial and temporal characteristics of extreme events. This is particularly important for precipitation extreme events which are still not well represented in climate models. Here, we compare South Korean station data with two observation-based gridded data sets (APHRODITE and ERA5-Land) and data from global high-resolution Community Earth System Model (CESM) simulations with an atmospheric resolution of about 25km. We find that the two observation-based data sets have a lower level of the 99th percentile than the station data, but that CESM reproduces extreme events better. Our study provides evidence for an overall historical decrease in very large extreme events in the station data, which is not the case in the two gridded data sets. However, changes in extremes are locally dependent as shown by local quantile regression analysis; where local historical increases in precipitation extremes are statistically significant. The spatial dependence of extreme precipitation events is not well reproduced by the two gridded data sets but well by CESM. The temporal clustering of precipitation extremes is well reproduced by all data sets. Compared to the present day simulation, the CESM simulation of a warmer climate state shows an overall increase in mean precipitation and precipitation extremes and regionally dependent changes in temporal clustering. The model results also provide evidence for a change in spatial dependence in a warmer climate with spatially larger extreme precipitation systems possible. Our results highlight the need to produce better observation-based gridded data sets and also the need to adapt to more intense and frequent extreme precipitation events in the future in South Korea. [ABSTRACT FROM AUTHOR]

Perturbations in extreme precipitation characteristics are investigated over the Volta Basin (VB) and its three subdomains (Sahel, Soudano-Sahel and Guinea Coast) for the early-21st (2030–2053) and mid-twenty-first centuries (2057–2080) under representative concentration pathways (RCPs) 4.5 and 8.5. Seven climate indices from the Expert Team on Climate Change Detection and Indices were selected to examine future extreme precipitation features. Owing to its performance over the West African sub region, CanESM2 model results were used with Global Precipitation Climatology Centre (GPCC v7) dataset serving as reference data. Results generally show lowering trends in extreme precipitation events over the VB. The declines in extremes were dominant in the Sahel and Soudano-Sahel zones with some degree of upsurges observed in the Guinea Coast. Spatially over the basin, wet spells (CWD) were projected to shorten under RCP 8.5 (~ 7–27 days/year) relative to RCP 4.5 (~ 8–30 days/year). Similar pattern was observed for dry spells (CDD) with ranges of ~ 64–198 days/year and ~ 61–186 days/year respectively for RCPs 4.5 and 8.5. As revealed, future alterations in precipitation events have the propensity to cause alternating drought or flood events. In this line, sustainable adaptation measures and coping strategies need to be devised in time to minimize the consequences of these events, particularly those on water resources availability and ecosystem functions and services. [ABSTRACT FROM AUTHOR]

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980–2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985–2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025–2049, F2: 2050–2074, F3: 2075–2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future. [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]

Mitigating precipitation extremes is a major issue due to destructive global warming and climate change. Heavy rainfall and drought have posed a threat to human life and ecology. That said, new strategies and new action plans are needed at local and global levels through needed cooperation from different stakeholders to handle the possible risks associated with precipitation extremes. Türkiye has become one of the most vulnerable countries involved in climate change due to its geographical location, rapid urbanization, and deforestation. Many forests have been destroyed to make room for agriculture, animal grazing as well as for manufacturing and construction. The impact has caused complications in landscapes. Precipitation extremes, such as heavy rainfalls and drought, are posing significant threats for many cities in Türkiye. In recent years Türkiye has faced a large number of extreme events regarding precipitation. In this line, the present study aims to explore the potential benefits of rainwater harvesting (RWH) in mitigating precipitation extremes by overviewing regulatory actions of rainwater harvesting and best practices worldwide. In addition an interview-based survey was conducted with domain experts in the water management field to better understand the current challenges of stormwater management in Türkiye and discuss the role of rainwater harvesting against precipitation extremes. The results of the study have shown that Türkiye has several problems with infrastructure to mitigate precipitation extremes, such as shortcomings in capacity and old water management systems, unseparated water collection and sewage systems, and lack of green infrastructure. In addition to urbanization, expansion in industry and tourism may cause water unavailability. The study has also indicated that many authorities around the globe try to boost RWH use by stipulating or encouraging RWH through incentives to save a large amount of water by implementing different projects. This research has argued that RWH promises several benefits thanks to its cost-effectiveness and contribution to water storage. Therefore, this study has recommended that policymakers should take immediate action against precipitation extremes by introducing new regulations, such as mandating rainwater harvesting for old buildings, industrial and touristic places. Preparing new guidelines and applying rooftop RWH systems that comply with Building Code requirements should also be considered for the widespread use of rainwater in rural and urban areas. [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]

In the present study, we have made an effort to examine the influence of dust aerosols on the precipitation over India, and six homogeneous rainfall zones of India during pre-monsoon (March to May) and monsoon (June to September) seasons. In this regard, a set of sensitivity experiments are performed using a regional climate model, namely, RegCM-4.7.0, for 11 years. Extensive analysis has been carried out to evaluate the model's performance in simulating the mean precipitation, its variability (spatial and temporal), dust aerosol optical depth, and monsoonal wind. It is observed that the model has the potential to capture the distinct features like prevailing north westerlies with less precipitation during pre-monsoon and southwesterlies with more precipitation during monsoon season, with systematic differences in magnitude while compared against observation. High dust aerosol optical depth is found during pre-monsoon season over central northeast, west central and northeast parts of India. Dust significantly increases precipitation parts over southern peninsula, west central and northwest during the monsoon season due to increases in wind at 850 hPa facilitating moisture transport from Arabian Sea. Interestingly, it is also noticed that dust aerosols modulate the magnitude of extreme precipitation indices over India implying importance in including dust aerosols effects in the climate models. [ABSTRACT FROM AUTHOR]

This work assesses the ability of nine Coupled Model Intercomparison Project phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) models and their ensemble mean to reproduce precipitation extremes over East Africa for the period 1995–2014. The model datasets are assessed against two observation datasets: CHIRPS and GPCC. The precipitation indices considered are CDD, CWD, R1mm, R10mm, R20mm, SDII, R95p, PRCPTOT, and Rx1day. The overall results show that HighResMIP models reproduce annual variability fairly well; however, certain consistent biases are found across HighResMIP models, which tend to overestimate CWD and R1mm and underestimate CDD and SDII. The HighResMIP models are ranked using the Taylor diagram and Taylor Skill Score. The results show that the models reasonably simulate indices, such as PRCPTOT, R1mm, R10mm, R95p, and CDD; however, the simulation of SDII CWD, SDII, and R20mm is generally poor. They are CMCC-CM2-VHR4, HadGEM31-MM, HadGEM3-GC31-HM, and GFDL-CM4. Conversely, MPI-ESM1-2-XR and MPI-ESM1-2-HR show remarkable performance in simulating the OND season while underestimating the MAM season. A comparative analysis demonstrates that the MME has better accuracy than the individual models in the simulation of the various indices. The findings of the present study are important to establish the ability of HighResMIP data to reproduce extreme precipitation events over East Africa and, thus, help in decision making. However, caution should be exercised in the interpretation of the findings based on individual CMIP6 models over East Africa given the overall weakness observed in reproducing mean precipitation. [ABSTRACT FROM AUTHOR]

The Yangtze River basin is a typical region of the world that has a well-developed economy but is also greatly affected by multiple climate extremes. An improved understanding of future climate trends and associated exposures in this region is urgent needed to address socioeconomic risks. This research aims to quantify historical and future projected population exposure to precipitation extremes in the Yangtze basin using meteorological records and downscaled climate models. The study found that the hazard zone for precipitation extremes during baseline period was primarily located in the mid-lower Yangtze basin, particularly around the Poyang Lake watershed. Climate projections for 2050 indicate a further increase in the occurrence of precipitation extremes in this hazard zone, while a decrease in extreme events is detectable in the upper Yangtze basin under higher radiative forcing. Future socioeconomic scenarios suggest a tendency for population growth and migration towards the lower Yangtze basin, resulting in aggravated climate risks in megacities. Multi-model simulations indicate that population exposure to precipitation extremes in the lower Yangtze basin will increase by 9–22% around 2050, with both climate and population factors contributing positively. Shanghai, Changsha, Hangzhou, Ganzhou, and Huanggang are identified as hotspot cities facing the highest foreseeable risks of precipitation extremes in the Yangtze basin.

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

This study assesses the long-term variability of extreme precipitation events in the Carpathian region of Poland in relation to climate change and its potential impact on river regime. Focusing on eight catchments with undisturbed runoff regimes, the research utilized daily data from May to October between 1951 and 2022. This data included precipitation totals from 23 gauging stations, discharge values from eight water level gauging stations, and gridded precipitation data from the E-OBS dataset. Additionally, future projections for 2026–2060 under two RCP scenarios (4.5 and 8.5) were generated using five GCM-RCM model combinations from the EURO-CORDEX simulations. The study explored the relationship between precipitation and discharge, using probability techniques to identify extreme events. This study highlights the strong linkages between extreme precipitation and discharge in the region, indicating an elevated flood risk due to increased precipitation variability and volume. Despite no significant trends in the frequency of precipitation extremes being observed in the historical analysis, an increase in seasonal precipitation totals is projected, although with considerable uncertainty. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are crucial components of the hydrological cycle and often produce flash floods. Given their impact, it is important to understand how they will change under a warming climate. This study uses a satellite‐ and radar‐based MCS tracking algorithm on convection‐permitting climate model simulations and examines changes in MCS properties and precipitation structures between historical and future simulations. An underestimation in MCS total precipitation is evident in historical simulation compared to observations, due to model's depiction of MCS precipitation area and summertime occurrence frequency. Under pseudo‐global warming, increases in MCS frequency and total warm season precipitation are observed, most notably in the southern U.S. The precipitation intensity and precipitating area generated by future MCSs also rises and results in an increase in precipitation volume. MCS precipitation structures are further classified into convective core and stratiform regions to understand how change in these structures contributes to future rainfall changes. In a warmer climate, the stratiform region demonstrates minimal change in size, but increases in mean precipitation rate and mean maximum precipitation rate by 15% and 29% are noted, respectively. A more robust future response is observed in the convective core region, with its size, mean precipitation rate and mean maximum precipitation rate increasing significantly by 24%, 37% and 42%, respectively. Finally, by examining the environmental properties of MCS initial condition, future intensification of convective rain may be attributed to a combined effect of substantial increases in atmospheric instability and moisture availability. Plain Language Summary: Thunderstorms that group together and grow to hundreds of kilometers in size and persist for more than a few hours are called mesoscale convective systems. They play an important role in Earth's water cycle and are often responsible for causing flash flooding events. Given their impact, understanding how they will change due to a warming climate is crucial. By analyzing the outputs from climate models, this study reveals future changes in various aspects of mesoscale storms behaviors, such as their frequency and where they occur. In addition, the rainfall patterns from the present and future mesoscale storms are compared. Our findings show that in a warmer climate, these storms will probably occur more frequently and bring more rain, especially in the southern parts of the United States. Individual mesoscale storms are also expected to contain larger rain areas and release heavier rainfall, which would contribute to an overall increase in rain volume. A shift from lighter rain to more moderate/heavy rain during these storms is also noticed, implying a potential rise in intense mesoscale storm events and heightened chances of flash floods in the future. Key Points: An Mesoscale convective system (MCS) tracking algorithm is applied to regional climate models to examine the potential future changes in their rainfall characteristicsAn evaluation between observations and historical simulations shows that the model underestimates MCS total precipitationFuture MCSs may become more frequent, more intense and produce higher rain volume, primarily due to the large changes in the convective core [ABSTRACT FROM AUTHOR]

While sub-daily precipitation extremes cause flash flooding and pose risk to life, longer precipitation extremes threaten infrastructure such as water supply dams. Frequent storm or floods events replenish water supplies, ensuring the health of our ecosystems, while rarer larger storms or floods cause damage to property and life. These differing impacts depend on both storm rarity and duration and are largely dependent on coincident atmospheric water vapour. Using a novel metric that quantifies the extent of concurrency that exists between precipitation and total water vapour extremes, large regional variations are identified across the globe. Tropical regions such as Northeast Africa and South/East Asia consistently exhibit greater concurrency across all precipitation durations. In contrast, areas of the extra-tropics, such as the Mediterranean and Northwest Americas, show a rapid decline in concurrency with increasing duration. However, for rare events of long duration, non-tropical regions maintain high concurrency. With the link between climate change and increasing total water vapour well established, these results suggest that flood events will increase globally, with increases most apparent for longer and rarer events. This work underscores the need for tailored regional strategies in managing extreme precipitation and flood events in the future.

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]

Present study explores the precipitation spectrum over Central India in changing climate using accurate precipitation estimates at fine resolution (hourly observations at 5 km resolution) from multi-spectral satellite observations using 24 years (1998–2021) of high-resolution data of precipitation over India during the summer monsoon. Though only a 24-year short period is perceived to be inadequate to explore the long-term changes in the precipitation spectrum, this study still represents the best possible way to explore the changes in short-lived intense precipitation events in changing climate in the high-resolution satellite era. Changes in precipitation extremes are linked with regional warming over India. A significant increase in heavy precipitation events is observed over Central India. Heavy precipitation events show an increase of about 137% over Central India for a unit degree increase in warming. Low and moderate precipitation events show a decrease of up to 54% over the region. Changes in extreme rain events are highest in July month. Results reported in this study highlight the importance of disaster preparedness against flash flooding due to increased heavy rain events. [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]

Understanding long-term variations in precipitation is crucial for identifying the effects of climate change and addressing hydrological and water management issues. This study examined the trends of the mean and four extreme precipitation indices, which are the max 1-day precipitation amount, the max 5-day precipitation amount, the consecutive wet days, and the consecutive dry days, for historical observations (1971–2000) and two future periods (2041–2060/2081–2100) under RCP2.6 and RCP8.5 emission scenarios over the Nile River Basin (NRB) at 11 major stations. Firstly, the empirical quantile mapping procedure significantly improved the performance of all RCMs, particularly those with lower performance, decreasing inter-model variability and enhanced seasonal precipitation variability. The Mann–Kendall test was used to detect the trends in climate extreme indices. This study reveals that precipitation changes vary across stations, scenarios, and time periods. Addis Ababa and Kigali anticipated a significant increase in precipitation across all periods and scenarios, ranging between 8–15% and 13–27%, respectively, while Cairo and Kinshasa exhibited a significant decrease in precipitation at around 90% and 38%, respectively. Wet (dry) spells were expected to significantly decrease (increase) over most parts of the NRB, especially during the second period (2081–2100). Thereby, the increase (decrease) in dry (wet) spells could have a direct impact on water resource availability in the NRB. This study also highlights that increased greenhouse gas emissions have a greater impact on precipitation patterns. This study's findings might be useful to decision makers as they create NRB-wide mitigation and adaptation strategies to deal with the effects of climate change. [ABSTRACT FROM AUTHOR]

Evaluating the susceptibility of regional climates to climate change provides a framework for realistically analyzing potential future climate changes. This paper investigates the impact of human activities on variations in extreme precipitation in Iran by evaluating data provided from 286 rain-gauge stations during 1967-2010 and general circulation simulation results of the CanESM2 model. This investigation was based on six forcing factors, including natural external factors (volcanic aerosols, solar radiation), anthropogenic and a combination of them, Green House Gases (GHGs), changes in land use, and anthropogenic aerosols. Seven precipitation indices, namely Rx1day (annual maximum 1-day precipitation), Rx5day (annual maximum 5-day precipitation), R10mm (annual count of days with daily precipitation exceeding 10 mm), R20mm (annual count of days with daily precipitation exceeding 20 mm), CDD (consecutive dry days), CWD (consecutive wet days), and PRCPTOT (annual total wet day precipitation), have been analyzed via the optimal fingerprint method. The results revealed that Rx1day, Rx5day and CWD increased, while R10mm, R20mm, CDD, and PRCPTOT decreased among which CDD and Rx1day indices showed significant variations, with values of 18.4% and 10.9%, respectively. Furthermore, the obtained results implied that only the impact of anthropogenic forcing, with a value of 1.4, was detected and attributed to variations in CDD. Additionally, anthropogenic forcing caused an increase in the return period of a 20-year event by 1.9 years for CDD. Although human-induced forcing factors presented marked trends in some cases, their effects were not generally detected and attributed to the change in the observations, apart from one exception. [ABSTRACT FROM AUTHOR]

Abstract Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.

Reanalysis-driven regional climate simulations using the Weather Research and Forecasting (WRF) model in New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) Version 2.0 are assessed for capturing precipitation extreme indices. Seven configurations of the WRF model driven by ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) for Australia from 1979 to 2020 at 20 km resolution are evaluated. We assess the spatiotemporal patterns of six selected Expert Team on Sector-Specific Climate Indices (ET-SCI) precipitation extremes by comparing regional climate model (RCM) simulations against gridded observations. The RCMs evaluated have varying levels of accuracy in simulating precipitation extremes. While they capture climatology and coefficient of variation of precipitation extremes relatively well, temporal correlation and trend reproduction present challenges. Some RCMs perform more effectively for specific extreme indices, while others encounter challenges in accurately replicating them. No single RCM excels in all aspects, highlighting the need to consider specific strengths when selecting RCMs for global climate model (GCM) driven simulations.

Extreme heat-precipitation events, such as heatwaves and extreme precipitation, can have substantial impacts on the population, particularly in urbanized watersheds. However, few studies have investigated individual and compound extreme heat-precipitation events, causing much valuable information loss for watershed climate risk management. This study focuses on the Pearl River Basin (PRB), a highly urbanized area in southern China, and aims to predict changes in population exposure to extreme heat-precipitation events. To achieve this, a ranked ensemble global climate model (GCM) was used to generate projections for individual extreme precipitation, heatwaves, and sequential and coincident heat waves and precipitation extremes (SHWPs and CHWPs) under three future scenarios (SSP-RCPs). The main findings of the study are as follows: Precipitation extremes represent increasing extreme days and intensity under all three scenarios across the PRB. Towards the end of the 21st century, the SSP5-8.5 scenario predicts that heat waves will last ten times longer than historical records. Comparing two types of compound extreme events, we conclude that the 21st century will see a near-term high risk for SHWPs and a long-term high risk for CHWPs in the PRB. Furthermore, in both individual and compound heat-precipitation events, five hotspot cities in the PRB (i.e., Guangzhou, Dongguan, Foshan, Shenzhen, and Huizhou) will face higher population exposure to extreme heat-precipitation events. These cities share common characteristics: key to economic development, coastal, and densely populated. This study can provide insight into extreme climate risk management in other tropical and subtropical basins.

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.

This work assesses the ability of nine Coupled Model Intercomparison Project phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) models and their ensemble mean to reproduce precipitation extremes over East Africa for the period 1995–2014. The model datasets are assessed against two observation datasets: CHIRPS and GPCC. The precipitation indices considered are CDD, CWD, R1mm, R10mm, R20mm, SDII, R95p, PRCPTOT, and Rx1day. The overall results show that HighResMIP models reproduce annual variability fairly well; however, certain consistent biases are found across HighResMIP models, which tend to overestimate CWD and R1mm and underestimate CDD and SDII. The HighResMIP models are ranked using the Taylor diagram and Taylor Skill Score. The results show that the models reasonably simulate indices, such as PRCPTOT, R1mm, R10mm, R95p, and CDD; however, the simulation of SDII CWD, SDII, and R20mm is generally poor. They are CMCC-CM2-VHR4, HadGEM31-MM, HadGEM3-GC31-HM, and GFDL-CM4. Conversely, MPI-ESM1-2-XR and MPI-ESM1-2-HR show remarkable performance in simulating the OND season while underestimating the MAM season. A comparative analysis demonstrates that the MME has better accuracy than the individual models in the simulation of the various indices. The findings of the present study are important to establish the ability of HighResMIP data to reproduce extreme precipitation events over East Africa and, thus, help in decision making. However, caution should be exercised in the interpretation of the findings based on individual CMIP6 models over East Africa given the overall weakness observed in reproducing mean precipitation.

This study investigates the future changes in precipitation extreme indices in the Lake Urmia Basin during the period 2021–2100 compared to the base period (1987–2016), using the Coupled Model Intercomparison Project Phase 5 models. Trend analysis was performed using Mann–Kendall test and Sen's estimator. The output of these models was downscaled by the Long Ashton Research Station Weather Generator method for the representative concentration pathway (RCPs) 4.5 and 8.5. A model averaging technique was employed to create an ensemble model. The results showed that the average precipitation of the basin will decrease by the end of the 21st century. The projection also showed that the consecutive dry day's index increases based on both scenarios. However, other indices (maximum 1-day precipitation, maximum 5-day precipitation, very wet days, consecutive wet days, simple daily intensity index, and wet-day precipitation) are reduced compared to the base period. Moreover, the slope of significant trends in the RCP8.5 is greater and more severe than that in RCP4.5. HIGHLIGHTS This study focuses on the future change of extreme rainfall in the Lake Urmia basin in Iran.; The projected results are decreasing trends in extreme precipitation and in wet days and increasing trends in dry days by the end of the 21st century.; This study predicts that the basin may face more drought in the future.; The results of this study have important implications for the management of water resources.;

Satellite products, such as the Integrated Multi-Satellite Retrievals of IMERG from the Global Precipitation Measurement (GPM) mission, have emerged as promising tools to analyze precipitation distribution and extremes, particularly in regions with low rain gauge density and sparse distribution, such as Brazil. However, regional validation of satellite data is crucial. In this context, the validation of GPM (IMERG) for the Parnaíba River Basin in northeastern Brazil is important due to its high hydrological potential and the presence of one of the largest expanding agricultural frontiers in the world. This study evaluates the estimation capacity of IMERG version 6 satellite data, including IMERG Early, Late, and Final products, for extreme precipitation in the Parnaíba River Basin from 2001 to 2020. A pixel point-to-point approach is used to compare the satellite estimates with observed precipitation data measured by rain gauges. Eight indices of extreme precipitation are analyzed, along with statistical measures such as bias, mean square error, root mean square error, probability of detection (POD), false alarm ratio (FAR), and the Kling-Gupta efficiency (KGE) index and its components. The results show that the IMERG Final estimates exhibit better agreement with in situ data at the daily scale compared to the IMERG Early and Late estimates. The lower Parnaíba region shows higher POD values, while the middle Parnaíba region exhibits higher KGE values, particularly in tropical climate areas. The IMERG products demonstrate different capabilities in observing extreme rainfall in the basin, with IMERG Final showing satisfactory results for 50 % of the analyzed indices, performing more robustly in capturing the PRCPTOT index and reasonably for CDD, RX5day, and R95p. We conclude that the IMERG Final product can be used as a data source for analyzing precipitation extremes in the Parnaíba River Basin, with bias adjustment recommended for better performance at the daily scale.

Extreme precipitation exhibits high temporal and spatial variability, and understanding this variability is crucial for designing hydraulic infrastructures and assessing the impacts of natural risks such as floods and droughts. The Simple Scaling (SS) model, which describes the dependence of extreme rainfall statistics on timescales, is used to estimate Intensity Duration Frequency (IDF) curves. This study aims to evaluate the validity of the Simple Scaling (SS) hypothesis for 14 rain gauges in Senegal and explore the existence of breaks in temporal scaling regimes (transition regimes). For the analysis of extreme characteristics, we considered the Annual Maximum Series (AMS) of precipitation in sub-hourly and supra-daily durations ranging from 10 min to 7 days. The empirical validation of the SS models was confirmed for the majority of the scaling intervals. We identified two scaling breaks, indicating the presence of three scaling regimes: short durations (SD), intermediate durations (ID), and long durations (LD). The spatial variation of scaling exponents reveals the existence of different scaling regimes across Senegal. The results are valuable for modeling the spatial distribution of scaling exponents, which can aid in characterizing IDF curves at ungauged locations.

Abstract 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.

Climate-related disaster impacts, such as loss of human life as its most severe consequence, have been rising globally. Some studies attribute this increase to population growth, while others point to climate change as the primary cause. However, empirical evidence linking climate change to disaster impacts remains limited, particularly in the Global South. This study addresses the impact attribution question in Nepal, a low-income and highly disaster-prone country. We applied a robust regression-based method that accounts for the role of hazard, exposure and vulnerability in flood and landslide mortality, using subnational scale empirical data from 1992 to 2021.Historically, flood and landslide mortality has been highest in central and eastern Nepal due to the stronger influence of the Indian monsoon. However, disaster impacts have surged in recent years in western Nepal, driven largely by an increase in extreme precipitation events. For example, a one standardized unit increase in maximum one-day precipitation increases flood mortality by 33%, and heavy rain days increases landslide mortality by 45%. In contrast, a one standardized unit increase in per capita income reduces landslide and flood mortality by 30% and 45%, respectively. While reductions in vulnerability have helped lower disaster mortality, population exposure has not played a significant role. Therefore, the rise in flood and landslide mortality, particularly in western Nepal, is primarily attributable to the increase in precipitation extremes linked to climate change. With climate change expected to further intensify such extremes, disaster mortality is likely to increase unless significant efforts are made to reduce vulnerability.

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]

Complex interaction between urbanization and climate change has been showing significant impacts on natural and human ecosystems. Increasing urban flooding and waterlogging are associated with urbanization. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) experiences a rapid and extensive urbanization, leading to intensified land use and cover changes. Concurrently, the frequent occurrence of extreme precipitation events pose great challenges of urban flood control and water resource management to GBA. This research statistically analyzes the spatiotemporal evolution characteristics of precipitation extremes from 1979–2018 which relates to the urbanization in GBA using various statistical methods including the Mann-Kendall test, bivariate Moran's test, and Spearman correlation analyses. The findings indicate that the impervious surface area in GBA exhibited a nonlinear growth trend from 1985–2018, particularly concentrated in the five major cities, i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan. GBA urbanization can be categorized into three stages including Stage I (pre-1990, no urbanization), Stage II (1991–2009, rapid urbanization), and Stage III (2010–2018, slow urbanization). Compared to cities with low urbanization, the highly urbanized areas of GBA, including Guangzhou, Foshan, Zhongshan, and Dongguan, show statistically significant increases in precipitation extremes. The increasing trends of seven extreme precipitation indices show significant positive, spatiotemporal correlations with the change rate of urbanization in GBA. Moreover, the influence of urbanization on precipitation extremes in highly urbanized regions of the GBA is progressively strengthened along with urban development. During the stage of slow urbanization, urbanization contributes to 56.13% of increase in annual precipitation totals in GBA, and its contribution to increases in precipitation extremes ranges from 20–80%. [ABSTRACT FROM AUTHOR]

Abstract 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.

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]

This study analyzed long-term extreme precipitation indices using 4 × 4 km gridded data obtained from the National Meteorological Agency of Ethiopia between 1981 and 2018. The study examined trends in extreme precipitation over three districts (Lay Gayint, Tach Gayint, and Simada) in the northwestern highlands of Ethiopia. Innovative Trend Analysis (ITA) and Mann–Kendall (MK) trend tests were used to study extreme precipitation trends. Based on the ITA result, the calculated values of nine indices (90% of the analyzed indices) showed significant increasing trends (p < 0.01) in Lay Gayint. In Tach Gayint, 70% (seven indices) showed significantly increasing trends at p < 0.01. On the other hand, 60% of the extreme indices showed significant downward trends (p < 0.01) in Simada. The MK test revealed that 30% of the extreme indices had significantly increasing trends (p < 0.01) in Lay Gayint. In Tach Gayint, 30% of the extreme indices showed significant increasing trends at p < 0.05, while 10% of the extreme indices exhibited significant increasing trends at p < 0.01. In Simada, 20% of the extreme indices showed significant increasing trends at p < 0.05. Overall, the results showed that the ITA method can identify a variety of significant trends that the MK test misses. [ABSTRACT FROM AUTHOR]

In this study, we have investigated changes in precipitation extremes and the population's exposure to these extremes during 2091–2099 in China's Beijing–Tianjin–Hebei (JJJ) region relative to the historical period of 1991–1999. First, the regional climate model RegCM4, with a hydrostatic dynamic core, was run for east Asia, including China, at a 12 km resolution for 1990–1999 and 2090–2099. This model is forced by global climate model (GCM) MPI-ESM1.2-HR under the middle shared socioeconomic pathways (SSP245). The first year was used as a model spinup. Then, the 12 km results were used to force RegCM4 with a non-hydrostatic dynamic core (RegcM4-NH) at a 3 km convection-permitting scale over the JJJ region during the historical and future periods. Future precipitation extremes were predicted to increase over the whole of China and its four subregions, while decreases were predicted over the JJJ region. This may partly be caused by lower increases in specific humidity over the JJJ region. The percentage contributions of the three components of total population exposure, i.e., changes in exposure due to changes in the population, precipitation extremes and the joint impact of the population and extremes, were then analyzed. Changes in the population and wet extremes were closely related to changes in the total exposure over the JJJ region. The population is the dominant factor that most impacts the total exposure to dry extremes. Finally, changes in future population exposure to precipitation extremes per degree of warming were quantified for the JJJ region. [ABSTRACT FROM AUTHOR]

The perception of spatio-temporal variability of winter precipitation covering dominant water resources and environment over Kashmir Himalaya is a pre-eminent perspective under current social interests. The main objectives of the study are, firstly, to unriddle the whole (1980–2020) and post-2000 trend in the winter season precipitation (WP), frequency of low-pressure systems (Lp-S), and western disturbances (WDs) to understand the footprints of climate change over the region; secondly, to evaluate the spatio-temporal variability of winter precipitation under the dominance of global teleconnection indices, using both station and reanalysis datasets. Modified Mann-Kendell test and a linear regression model were utilized to extract the trend and significance in WP, Lp-S, and WDs. The results reveal that WP shows an insignificant decreasing trend (1980–2020), with an increasing trend post-2000, while a decreasing trend is seen during post-2000 in the frequency of Lp-S and WDs. The contribution of WP to annual precipitation shows decadal variability with a decreasing trend post-2000. The anatomy of winter extreme precipitation days has shown a decreasing trend (1980–2020), with a robust coupled increasing trend with WP post-2000. The dynamics of ten severe WD-based extreme precipitation events show that a cyclonic circulation and trough connects the Arabian Sea and the Himalayas, bringing additional moisture to the study region and resulting in extreme precipitation. The cogency of global teleconnections on the variability of WP shows that a pattern with positive phase of NAO, ENSO, and a negative phase in Siberian high intensifies the WD-based precipitation. The study discovers its applicability for the regional forecast system in predicting the winters. [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]