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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 Taklimakan Desert (TD) is the largest desert in China located in the Tarim Basin (TB) in China's arid region. This study is a review of the change in precipitation and its extremes since 1961 and the high-impact extreme precipitation events in 2012-2021, particularly in 2021, with a focus on the TD along with the surrounding oases and mountainous regions. The TB has experienced significantly warmer and wetter trends since 1961, and extreme rainfall has increased significantly in the TD and its surrounding areas during the 2000s. In the TB, the year 2021 was identified as the 4th warmest for 1961-2021, and was remembered for unprecedented extreme events. Three high-impact extreme events that occurred in 2021 are highlighted, including extreme heavy rainfall over Hetian in mid-June. The earliest extreme rainfall event occurred over North Bazhou in early spring, and the strongest heavy snowfall over Baicheng in April. In addition, we also discussed the underlying physical mechanisms of extreme events over the TB and proposed novel perspectives and unresolved questions on the sciences of heavy rainfall in arid regions. Our results provide a reference for the physical mechanism, attribution, and high-resolution modeling of extreme events. [ABSTRACT FROM AUTHOR]

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Purpose: Soil erosion and sediment transport are often induced by a few extreme precipitation events. Currently, there is limited information on the effects of changes in precipitation extremes on river sediment load over a long time period at the basin scale. The objectives of this study were to quantify the effects of variations in precipitation extremes on sediment load in the Second Songhua River Basin (SSRB), northeast China. Materials and methods: Daily precipitation data from 10 meteorological stations within the SSRB and sediment load data during 1960–2014 from the basin outlet hydrological station were collected. Six extreme precipitation indices, such as erosive precipitation (ERPTOT), wet-day precipitation (PRCPTOT), very wet day precipitation (R95pTOT), flood season precipitation (FSPTOT), maximum 1-day precipitation (RX1day) and heavy precipitation (HP), were used to detect the precipitation extremes. The Mann–Kendall non-parametric trend analysis was used to analyze the dynamic changes in precipitation extremes and sediment load. The double cumulative curve method was used to assess the effects of changes in precipitation extremes on sediment load. Results and discussion: The results indicated that six extreme precipitation indices showed periodic variations from 1960 to 2014, while they did not show any significant change trend in the SSRB (p > 0.05); however, the sediment load showed an extremely significant decreasing trend (p < 0.01) over the past 55 years. Sediment load significantly correlated with six precipitation extremes indices (p < 0.05). Compared with the baseline period (1960–1977), the sediment load was reduced by 1.12 × 106 ton per year (1977–2014). The contribution of precipitation extremes to the sediment load ranged from -22.83% to 6.97%. The corresponding contribution of the non-precipitation factor to sediment load was from 93.03% to 122.83%. Conclusions: The change in precipitation extremes was not the main reason for the sediment load reduction. Sediment load reduction in the SSRB was due to the interactions between multiple factors. Non-precipitation factors, such as reservoir construction, were the main reasons for the sediment load reduction. These findings are useful to understand the variation of river sediment load and to adopt countermeasures to relieve potential risk of precipitation extremes. [ABSTRACT FROM AUTHOR]

Extreme climate events can pose huge threats to human safety and industrial and agricultural production. Therefore, investigating the time of emergence (TOE) of climate extremes is essential to policymakers, especially residents suffering from related climate disasters. However, there is still a lack of work focused on the TOE of climate extremes over the whole global land and aggregated climate subtypes. Here, we employed phase 5 of the Coupled Model Intercomparison Project (CMIP5) multi-models to calculate the TOE of 27 widely used Expert Team for Climate Change Detection and Indices (ETCCDI) indices for the sake of analyzing the intensity, frequency, and duration of extreme temperature and extreme precipitation characteristics comprehensively based on Köppen-Geiger climate classification. We found the temperature indices change prior to the precipitation indices in general, and are more uniformly in spatial distributions of TOE. Regionally, the warming and wetting trends are obvious in Tropical areas. Arid areas are likely to witness tiny changes for most precipitation indices. Temperate areas are the most similar climate subtype to the mean state of the global land. The substantially earlier TOE of precipitation indices in Continental and Polar areas highlights the need for an early warning system. The findings of this study will help policymakers take targeted preventive measures to deal with specific extreme events in an adaptation to local conditions.

Two or more spatio-temporally co-located meteorological/climatological extremes (co-occurring extremes) place far greater stress on human and ecological systems than any single extreme could. This was observed during the California drought of 2011–2015 where multiple years of negative precipitation anomalies occurred simultaneously with positive temperature anomalies resulting in California’s worst drought on observational record. The large-scale drivers which modulate the occurrence of extremes in two or more variables remains largely unexplored. Using California wintertime (November–April) temperature and precipitation as a case study, we apply a novel, nonparametric conditional probability distribution method that allows for evaluation of complex, multivariate, and nonlinear relationships that exist among temperature, precipitation, and various indicators of large-scale climate variability and change. We find that multivariate variability and statistics of temperature and precipitation exhibit strong spatial variation across scales that are often treated as being homogeneous. Further, we demonstrate that the multivariate statistics of temperature and precipitation are highly non-stationary and therefore require more robust and sophisticated statistical techniques for accurate characterization. Of all the indicators of the large-scale climate conditions we studied, the dipole index explains the greatest fraction of multivariate variability in the co-occurrence of California wintertime extremes in temperature and precipitation.

Abstract 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.

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.

Abstract 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.

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 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]

The article presents the results of a study on the assessment of modern space–time trends of extreme values of air temperature and precipitation in 42 meteorological stations throughout Kazakhstan for the period from 1971 to 2020. Spatial and temporal analysis of the distribution of specialized climatic indices was recommended by the WMO climatology commission and an assessment of their trends was carried out. Spatial heterogeneity was revealed in terms of the degree of manifestation of changes and trends. Temperature indices are shown to confirm the overall warming trend. The division of the territory of Kazakhstan by the degree of manifestation of climate change into the southwestern and northeastern half was revealed. Extreme trends are most pronounced in the southwestern half, where a significant trend has been identified both for an increase in extremely high daytime and extremely low night temperatures. The calculated trends in temperature indices are generally significant, but the significance is mainly not ubiquitous; the trends are significant only in certain parts of Kazakhstan. WSDI and CSDI trends were found to confirm a widespread increase in the overall duration of heat waves and a reduction in the overall duration of cold waves. No significant extreme effects were found in the sediments. It is confirmed that Kazakhstan has weak, statistically insignificant, positive and negative trends in the maximum duration of the non-traveling period. Precipitation index trends, unlike temperature ones, are statistically insignificant in most of the country. [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]

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.

Cyclonic atmospheric vortices of varying intensity, collectively known as low-pressure systems (LPS), travel northwest across central India and produce more than half of the precipitation received by that fertile region and its ∼600 million inhabitants. Yet, future changes in LPS activity are poorly understood, due in part to inadequate representation of these storms in current climate models. Using a high-resolution atmospheric general circulation model that realistically simulates the genesis distribution of LPS, here we show that Indian monsoon LPS activity declines about 45% by the late 21st century in simulations of a business-as-usual emission scenario. The distribution of LPS genesis shifts poleward as it weakens, with oceanic genesis decreasing by ∼60% and continental genesis increasing by ∼10%; over land the increase in storm counts is accompanied by a shift toward lower storm wind speeds. The weakening and poleward shift of the genesis distribution in a warmer climate are confirmed and attributed, via a statistical model, to the reduction and poleward shift of low-level absolute vorticity over the monsoon region, which in turn are robust features of most coupled model projections. The poleward shift in LPS activity results in an increased frequency of extreme precipitation events over northern India.

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]

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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]

Precipitation is a key meteorological component that is directly related to climate change. Quantifying the changes in the precipitation bioclimate is crucial in planning climate-change adaptation and mitigation measures. Southeast Asia (SEA), home to the world's greatest concentration of ecological variety, needs reliable monitoring of such changes. This study utilized the global-climate models from phase 6 of coupled model intercomparison project (CMIP6) to examine the variations in eight precipitation bioclimatic variables over SEA for two shared socioeconomic pathways (SSPs). All indicators were studied for the near (2020–2059) and far (2060–2099) futures to provide a better understanding of the temporal changes and their related uncertainty compared to a historical period (1975–2014). The results showed a high geographical variability of the changes in precipitation-bioclimatic indicators in SEA. The mainland of SEA would experience more changes in the bioclimate than the maritime region. The multimodel ensemble (MME) showed an increase in mean annual rainfall of 6.0–12.4% in most of SEA except the Philippines and southern SEA. The increase will be relatively less in the wettest month (15%) and more in the driest month (20.7%) in most of SEA; however, the precipitation in the wettest quarter would increase by 2.85%, while the driest quarter would decrease by 1.0%. The precipitation would be more seasonal. In addition, the precipitation would increase over a larger area in the wettest month than in the driest month, making precipitation vary more geographically. [ABSTRACT FROM AUTHOR]

Studying changes in precipitation extremes are important due to the possible increase in heavy rainfall, drought, and the occurrence of summer storms. Therefore, in this study, the trend of variations in precipitation extremes in the Middle East and North Africa (MENA) in the historical period (1985–2014) and the future period (2021–2050) was investigated. For this purpose, five precipitation extremes indices, including R10mm, R20mm, R95p, R99p, and PRCPTOT, and modified Man–Kendall trend test were used to examine the changes. To project changes in the future period, the multi-model mean of 12 GCMs from CMIP6 under three Shared Socioeconomic Pathway (SSP) scenarios including SSP1-2.6, SSP3-7.0, and SSP5-8.5 was utilized. The spatial distribution of the trend of changes in precipitation limits in the historical period showed that by moving from north to south and from west to east of the MENA region, all the studied indices have an increasing trend, so that the highest rate of decline is related to the countries of North Africa and also the PRCPTOT index and in Algeria, with a modified Man–Kendall coefficient of − 2.9. The highest increasing trend of the studied indices is related to the Middle East countries, especially countries such as Iran, Saudi Arabia, and Iraq with a modified Man–Kendall coefficient between 1.1 and 2.4, depending on the various indices. The distribution of variation trend in the future period also showed that the trends are similar to the historical period, with the difference that the downward trends in the studied indices are adjusted and the upward trends, especially based on the SSP5-8.5 scenario, would be intensified. Thus, the annual total precipitation on wet days (PRCPTOT) decreased and the annual count of days with 10- and 20-mm precipitation (R10mm and R20mm) as well as annual total PRCP with 95th and 99th percentiles (R95p and R99p) increased, with the largest increase in the Middle East and East of the MENA region; which indicates that despite the decrease in precipitation amount, the precipitation extremes and their intensities will increase. [ABSTRACT FROM AUTHOR]

Non-stationarity of climatic variables (e.g., temperature and precipitation) due to Climate Change (CC) can affect the migration processes of radionuclides released from nuclear activities. In this paper, a framework of analysis is developed to predict the evolution in time of contaminant concentration and fluence under different Climatic Boundary Conditions (CBCs) of precipitation scenarios provided by a climate model integrated with an accurate physical coupled hydraulic-transport model. A case study is worked out with respect to the migration of a radioactive contaminant ( 232 Th) at Kirtland Air Force Base (Albuquerque, New Mexico, USA), for which the different CBCs considered are: i) stationary and ii) non-stationary precipitation. The effects of such alternative hypotheses on the physical modelling results are analysed, using a cross-wavelet analysis. It is shown that fluence is strongly affected by precipitation extremes, more than concentration, and it is claimed that a daily scale on the information and data of CBCs is necessary to model, with sufficient accuracy, the migration process and properly assess the impact of future CC on groundwater contamination., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Climate disasters have a high impact on farming communities in terms of crop loss or reduced income. In the context of disasters, resilience is defined as the capacity to absorb its impacts, bounce back and even improve their previous status. The recent past two disasters namely Cyclone Thane (2011) and the South Indian floods (2015) had caused major crop loss in Cuddalore, India. A Farmer Resilience Index (FRI) has been assessed at the household level using primary data from 93 households (total) in Silambimangalam and Chinnakomatti villages in Cuddalore, with respect to the 2011 and 2015 disasters. The index has 18 parameters and 55 variables under four dimensions, namely, economic, social, technical, and physical. Farmers in both villages have average resilience to precipitation extremes, with FRI of 0.61 and 0.54, respectively. Seventy percent of the total samples are marginal farmers who have the lowest FRI of 0.47 and 4.3% are medium farmers having FRI of 0.83. Marginal farmers are poor and typically belong to lower castes with their farmlands located at lower elevations, which diminish their physical and economic resilience. The outcomes of the index reveal the current adaptive capacities of the farmers and have the potential to support future planning decisions. HIGHLIGHTS Resilience assessment is needed to improve adaptive capacities for future climate disasters.; Farmer Resilience Index (FRI) has been used to assess resilience.; Communities on the coast have better resilience and may be attributed to a reaction to a disaster experience.; Large farmers have improved resilience and reduced risk of income loss from a climate disaster.; The FRI has revealed the efforts taken by the farmers to address the impacts of a precipitation disaster.;

The gap in resolution between existing global climate model output and that sought by decision-makers drives an ongoing need for climate downscaling. Here we test the extent to which developments in deep learning can out-perform existing statistical approaches for downscaling historical rainfall in the highly complex terrain setting of New Zealand. While deep learning removes the need for manual feature selection when extracting spatial-temporal information from predictor fields, several key considerations need to be addressed. These include: the chosen complexity of the network architecture, suitable loss functions tailored to the problem, as well as input data considerations of domain size and amount of training data required to provide adequate out-of-sample generalization. Sensitivity testing to these considerations reveals that a relatively simple convolutional neural network (CNN) architecture with carefully selected loss functions can considerably outperform existing statistical downscaling models based on multiple linear regression with manual feature selection. When aggregated across the entire region, the fraction of explained variance on wet days increased from 0.35 to 0.52, the root-mean-squared error reduced by over 20% and percentage biases for the 90th percentile of rainfall improved by over 25%. Using interpretable machine learning methods, we demonstrate that the CNN has been capable of self-learning physically plausible relationships between the large-scale atmospheric environment and extreme localized rainfall events. The historical performance and physical interpretability documented here lends support for wider development and application of deep learning in climate downscaling.

Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25∘×0.25∘ grid (about 27 × 27 [km]). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity.

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.

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]

Rapid urbanization plays an indelible role in modifying local climate, with more extreme precipitation in urban areas. Understanding the mechanism of urban-induced precipitation changes and quantifying the potential effects of urbanization on the changes in precipitation extremes have become hotspot issues in hydrometeorology. We examine the spatiotemporal changes of precipitation extremes over the Pearl River Delta region in China using the homogenized daily precipitation dataset from the period 1961–2017, and quantify the urbanization effects on these changes. Most of the extreme precipitation indices show increasing trends, but only the mean precipitation intensity has a significant increase. Urbanization could induce the intensification of extreme precipitation, with a higher amount, intensity, and frequency of precipitation extremes and a larger magnitude of their trends in urban areas by comparison with those rural areas. Moreover, high-level urbanization tends to make a greater contribution to the temporal changes in precipitation extremes, indicating that urbanization effects on precipitation extremes may be related to urbanization levels. However, urbanization level shows little effect on the changes in the spatial patterns of precipitation extremes, with similar spatial distribution in different urbanization stages. Our findings highlight the important role of urbanization in precipitation extremes and offer insights into the feedback of anthropogenic changes into variations in precipitation extremes. [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]

This study investigates the future changes in dangerous precipitation extremes with multiyear return periods and the population exposure across China at the 1.5–4°C warming levels via the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that the simulations project more frequent dangerous precipitation extremes across China under the warmer climate regardless of the shared socioeconomic pathway (SSP), with more substantial occurrence increases at the high warming levels. Consequently, the population exposure to dangerous precipitation extremes is anticipated to increase persistently in most regions of China except for some parts of northwestern China and the Tibetan Plateau. For the events estimated to occur once every 10 years, the 1.5, 2.0, 3.0, and 4.0°C warming relative to the current state will result in approximately 29.9, 47.8, 72.9, and 84.3% increases in the aggregated population exposure over China under the SSP5-8.5 scenario, respectively. However, the exposure change is somewhat subject to the emission scenarios, with larger proportional increases under the regional-rivalry scenario of SSP3-7.0 compared to the fossil-fueled development scenario of SSP5-8.5. The increased exposure under all the scenarios is primarily attributed to the climate change factor, and the population change and their interaction component make a minor contribution. Furthermore, compared to the 2.0°C warmer climate, the 0.5°C less warming under the 1.5°C climate can trigger remarkable decreases of 16.5–20.8% for exposure to once-in-decade events over China. Additionally, the changes in the occurrence and exposure are much larger for the rarer events. Hence, our analyses indicate that limiting warming to 1.5°C is beneficial to reducing the impacts associated with precipitation extremes across China, particularly for the more extreme events.

It is reported that the China–Pakistan Economic Corridor has been affected by extreme precipitation events. Since the 20th century, extreme weather events have occurred frequently, and the damage and loss caused by them have increased. In particular, the flood disaster caused by excessive extreme precipitation seriously hindered the development of the human society. Based on CRiteria Importance Through Intercriteria Correlation and square root of generalized cross-validation, this study used intensity–area–duration to analyze the trend of future extreme precipitation events, corrected the equidistance cumulative distribution function method deviation of different future scenario models (CESM2, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6) and evaluated the simulation ability of the revised model. The results showed that: 1) the deviation correction results of CNRM-CM6-1 in the Coupled Model Intercomparison Project Phase (CMIP) 6 could better simulate the precipitation data in the study area, and its single result could achieve the fitting effect of the CMIP5 multimodel ensemble average; 2) under CNRM-CM6-1, the frequency of extreme precipitation events under the three climate scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) presents interdecadal fluctuations of 3.215 times/10A, 1.215 times/10A, and 5.063 times/10A, respectively. The average impact area of extreme precipitation events would decrease in the next 30 years, while the total impact area and the extreme precipitation events in a small range would increase. Under the future scenario, the increase rate of extreme precipitation was highest in August, which increased the probability of extreme events; 3) in the next 30 years, the flood risk had an obvious expansion trend, which was mainly reflected in the expansion of the area of high-, medium-, and low-risk areas. The risk zoning results obtained by the two different flood risk assessment methods were different, but the overall risk trend was the same. This study provided more advanced research for regional flood risk, reasonable prediction for flood risk under future climate models, and useful information for flood disaster prediction in the study area and contributes to the formulation of local disaster prevention and reduction policies.

It is common to conceptualize wetlands as a nature-based flood defense for improving basin resilience to climate change. Yet, such a solution is often ignored when projecting or assessing river flood risk under future climate change. To fill this gap, we apply a hydrological modeling platform integrating wetland modules to a 297,000-km2 large river basin in Northeast China under different climate change scenarios. The overarching goal of this study is to predict future precipitation extremes and flood events, and to explore whether and to what extent wetlands can effectively mitigate the risk of future flood at the basin scale. We first assessed the trend in future precipitation extremes extracted from multi-global climate models (GCMs) and found that the future precipitation extremes will increase both under RCP4.5 and RCP 8.5 scenarios. The increasing precipitation extremes, therefore, are projected to bring about higher flood risks with the increase in warming levels. Wetlands can suppress flood duration, mean flow, volume, and peak by 1.9–10.2%, 4.6–7.1%, 8.7–15.7% and 12.5–14.1% under future climate change in this large river basin, respectively. Our study found that wetlands can attenuate the risk of floods with a 2- and 5-year return period to a great extent. However, the wetland mitigation function is limited to floods with a 10-, 20-, and 50- return, and even failed to extreme floods with a 100- and 200-year return. These findings imply that future precipitation extremes will cause flood risks in this large river basin that cannot be mitigated by its wetlands, and substantial wetland restoration is needed to enhance the capacity of their service in order to improve the basin’s resilience to future flood risks.

Here, we investigate the precipitation regionalization and the spatial variability of rainfall extremes, using a 47-year long station-based dataset from western central Morocco, a region with marked topographic and climatic variations. The principal component analysis revealed three homogeneous rainfall regimes, consistent with topographic features: the coastal area receives heavy rainfall during autumns and winters, whereas the inner lowlands, in the middle of the study area, are characterized by an overall rainfall deficit regardless of their high water demand for irrigation, and the highest rainfall amounts take place in the mid-mountain area, including the summer seasons. Furthermore, the frequency analysis of daily rainfall extremes revealed high ten-year precipitation amounts in the coastal region (about 88 mm) and exceptional daily precipitation for longer return periods (182 mm for a 100-year period). Using artificial neural networks, the spatialization of these extreme precipitation events shows that they increase from the plain to the Atlas mountains and especially from the plain to the Atlantic Ocean. The spatial distribution of extreme precipitation highlights the areas where stormwater management needs to be improved, such as efficient stormwater drainage, and where floods are more likely to take place in the future.

The stationary assumption for the traditional frequency analysis of precipitation extremes has been challenged due to natural climate variability or human intervention. To overcome this challenge, this paper, taking Heihe River basin as the case study, performed the frequency analysis by developing a nonstationary GEV model for those seasonal maximum daily precipitation (SMP) time series with nonstationary characteristics by employing the GEV conditional density estimation network. In addition, the confidence intervals (CIs) of estimated return levels were also investigated by using the residual bootstrap technique. Results showed that, 7 of 12 SMP series were nonstationary. The parameters in the nonstationary model were specified as functions of time varying or correlated climate indices varying covariates. The frequency analysis showed that the return levels varied linearly or nonlinearly with covariates. Precipitation extremes with the same magnitude in the study area were found to be occurring more frequently in the future. The CIs of such return levels increased with time passing, especially those from the more complex GEV11 model, embedding a nonlinear increasing trend in model scale parameters. It implied that the increase of model complexity is likely to result in the increase of uncertainty in estimates.

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.

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]