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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.

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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.

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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.

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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.

El aumento de las temperaturas está incrementando los extremos de precipitación, pero las respuestas de los artrópodos a las fluctuaciones climáticas siguen siendo poco conocidas. Aquí, utilizamos la variación espaciotemporal en el clima montano tropical como un experimento natural para comparar la importancia de los factores bióticos versus abióticos en la regulación de la biomasa de artrópodos. Combinamos datos de campo intensivos de artrópodos, fenología de las hojas y clima in situ a lo largo de un gradiente altitudinal de 1700 a 3100 m y un gradiente de precipitación, junto con experimentos de resistencia a la desecación y modelos multi-decenales. Encontramos evidencia limitada para los factores bióticos con aumentos débiles en algunos taxones de herbívoros en arbustos con hojas nuevas, pero no hubo efectos a escala de paisaje en la fenología de la hoja, que rastreaba la luz y la cubierta de nubes. En cambio, las precipitaciones explicaron la amplia variabilidad interanual con una biomasa máxima en precipitaciones intermedias (130 mm mes−1 ), ya que los tres meses de precipitaciones altas y bajas redujeron los artrópodos a la mitad. Basándose en 50 años de precipitación regional, nuestro modelo dinámico de artrópodos predijo cambios en el momento de los máximos de biomasa dentro del bosque nuboso antes de que las comunidades de plantas hicieran la transición al bosque seco estacional caducifolio (precipitación media anual 1000-2500 mm vs. <800 mm). La magnitud de las lluvias fue el principal factor, pero durante la alta insolación solar, el "poder de secado del aire" (VPDmax ) redujo la biomasa en cuestión de días, lo que contribuyó a la sequía relacionada con El Niño-Southern Oscillation (ENSO). Destacando los riesgos de la sequía, los experimentos demostraron la susceptibilidad de toda la comunidad a la desecación, excepto en el caso de algunas orugas en las que la coloración a base de melanina parece reducir los efectos de la desecación por evaporación. En resumen, proporcionamos múltiples líneas de evidencia de que varios meses de fuertes lluvias o sequías reducen la biomasa de artrópodos independientemente de las plantas de raíces profundas con el potencial de desestabilizar las redes alimentarias de los insectívoros.

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Sub-daily precipitation extremes could intensify with temperature at a higher rate than the scaling for daily precipitation extremes, posing increasing risks to natural ecosystem and human society in the era of global warming. A systematic investigation of the climatology and spatiotemporal changes in sub-daily precipitation extremes is of paramount importance to inform future precipitation projection as well as to guide climate adaptation. Here, leveraging a newly proposed set of sub-daily extreme precipitation indices, we examine the climatology and changes in hourly precipitation extremes in mainland China across the major river basins during the warm period of 1970-2018. Our results show that the southern and eastern parts of China tend to experience more frequent hourly precipitation extremes with larger intensity, and the Pearl river basin has the most frequent and intense extreme precipitation at hourly timescale. The Southeast and Yangtze river basins and the mainland China as a whole have field significantly increasing trends in average and extreme precipitation intensities as well as in extreme precipitation frequencies. The intensification signals in hourly precipitation extremes of mainland China seem to emerge from internal climate variability around 2010, whereas average precipitation intensity since 1970 could become field significant earlier than 1999. Besides, we note a marked shift in the probability distributions of the extreme indices, with a wetting tendency toward more frequent and more intense precipitation extremes from the 1970-1999 period to the recent two decades in the 21st century. Our findings provide an alternative line of evidence for changes in precipitation extremes at hourly timescale over China and could contribute to societal decision-making for climate adaptation.

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It is widely recognized that future rainfall extremes will intensify. This expectation is tied to the Clausius-Clapeyron (CC) relation, stating that the maximum water vapour content in the atmosphere increases by 6-7% per degree warming. Scaling rates for the dependency of hourly precipitation extremes on near-surface (dew point) temperature derived from day-to-day variability have been found to exceed this relation (super-CC). However, both the applicability of this approach in a long-term climate change context, and the physical realism of super-CC rates have been questioned. Here, we analyse three different climate change experiments with a convection-permitting model over Western Europe: simple uniform-warming, 11-year pseudo-global warming and 11-year global climate model driven. The uniform-warming experiment results in consistent increases to the intensity of hourly rainfall extremes of approximately 11% per degree for moderate to high extremes. The other two, more realistic, experiments show smaller increases-usually at or below the CC rate-for moderate extremes, mostly resulting from significant decreases to rainfall occurrence. However, changes to the most extreme events are broadly consistent with 1.5-2 times the CC rate (10-14% per degree), as predicted from the present-day scaling rate for the highest percentiles. This result has important implications for climate adaptation. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

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

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Climate change has intensified the hydrologic cycle globally, increasing the magnitude and frequency of large precipitation events, or deluges. Dryland ecosystems are expected to be particularly responsive to increases in deluge size, as their ecological processes are largely dependent on distinct soil moisture pulses. To better understand how increasing deluge size will affect ecosystem function, we conducted a field experiment in a native semiarid shortgrass steppe (Colorado, USA). We quantified ecological responses to a range of deluge sizes, from moderate to extreme, with the goal of identifying response patterns and thresholds beyond which ecological processes would not increase further (saturate). Using a replicated regression approach, we imposed single deluges that ranged in size from 20 to 120 mm (82.3rd to >99.9th percentile of historical event size) on undisturbed grassland plots. We quantified pre- and postdeluge responses in soil moisture, soil respiration, and canopy greenness, as well as leaf water potential, growth, and flowering of the dominant grass species (Bouteloua gracilis). We also measured end of season above- and belowground net primary production (ANPP, BNPP). As expected, this water-limited ecosystem responded strongly to the applied deluges, but surprisingly, most variables increased linearly with deluge size. We found little evidence for response thresholds within the range of deluge sizes imposed, at least during this dry year. Instead, response patterns reflected the linear increase in the duration of elevated soil moisture (2-22 days) with increasing event size. Flowering of B. gracilis and soil respiration responded particularly strongly to deluge size (14- and 4-fold increases, respectively), as did ANPP and BNPP (~60% increase for both). Overall, our results suggest that this semiarid grassland will respond positively and linearly to predicted increases in deluge size, and that event sizes may need to exceed historical magnitudes, or occur during wet years, before responses saturate.

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Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986-2005) and future periods (2016-2035 and 2046-2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5-SSP3 scenario produces the highest global population exposure for 2046-2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6-SSP1 scenario produces the highest global GDP exposure for 2046-2065, with a 5.56-fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $-days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64-77% and 78-91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

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Convective organization has the potential to impact the strength of precipitation extremes, but numerical models disagree about this influence. This study uses satellite observations to investigate the link between the mesoscale organization of deep convection and precipitation extremes in the Tropics. Extremes in domain-averaged precipitation are found mostly over the western Pacific and Indian Ocean warm pools, and they primarily depend on the number of deep convective entities within the domain. On the other hand, extremes in local precipitation are found primarily over land, and they increase with the degree of convective organization. Therefore, this observational study shows evidence for a modulation of the strength of tropical precipitation extremes by the spatial organization of deep convection, especially over land.

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Insect declines are being reported worldwide for flying, ground, and aquatic lineages. Most reports come from western and northern Europe, where the insect fauna is well-studied and there are considerable demographic data for many taxonomically disparate lineages. Additional cases of faunal losses have been noted from Asia, North America, the Arctic, the Neotropics, and elsewhere. While this review addresses both species loss and population declines, its emphasis is on the latter. Declines of abundant species can be especially worrisome, given that they anchor trophic interactions and shoulder many of the essential ecosystem services of their respective communities. A review of the factors believed to be responsible for observed collapses and those perceived to be especially threatening to insects form the core of this treatment. In addition to widely recognized threats to insect biodiversity, e.g., habitat destruction, agricultural intensification (including pesticide use), climate change, and invasive species, this assessment highlights a few less commonly considered factors such as atmospheric nitrification from the burning of fossil fuels and the effects of droughts and changing precipitation patterns. Because the geographic extent and magnitude of insect declines are largely unknown, there is an urgent need for monitoring efforts, especially across ecological gradients, which will help to identify important causal factors in declines. This review also considers the status of vertebrate insectivores, reporting bias, challenges inherent in collecting and interpreting insect demographic data, and cases of increasing insect abundance.

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Ongoing intensification of the hydrological cycle is altering rainfall regimes by increasing the frequency of extreme wet and dry years and the size of individual rainfall events. Despite long-standing recognition of the importance of precipitation amount and variability for most terrestrial ecosystem processes, we lack understanding of their interactive effects on ecosystem functioning. We quantified this interaction in native grassland by experimentally eliminating temporal variability in growing season rainfall over a wide range of precipitation amounts, from extreme wet to dry conditions. We contrasted the rain use efficiency (RUE) of above-ground net primary productivity (ANPP) under conditions of experimentally reduced versus naturally high rainfall variability using a 32-year precipitation-ANPP dataset from the same site as our experiment. We found that increased growing season rainfall variability can reduce RUE and thus ecosystem functioning by as much as 42% during dry years, but that such impacts weaken as years become wetter. During low precipitation years, RUE is lowest when rainfall event sizes are relatively large, and when a larger proportion of total rainfall is derived from large events. Thus, a shift towards precipitation regimes dominated by fewer but larger rainfall events, already documented over much of the globe, can be expected to reduce the functioning of mesic ecosystems primarily during drought, when ecosystem processes are already compromised by low water availability.

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This study aims to characterize future changes in precipitation extremes over China based on regional climate models (RCMs) participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX)-East Asia project. The results of five RCMs involved in CORDEX-East Asia project that driven by HadGEM2-AO are compared with the simulation of CMA-RegCM driven by BCC-CSM1.1. Eleven precipitation extreme indices that developed by the Expert Team on Climate Change Detection and Indices are employed to evaluate precipitation extreme changes over China. Generally, RCMs can reproduce their spatiotemporal characteristics over China in comparison with observations. For future climate projections, RCMs indicate that both the occurrence and intensity of precipitation extremes in most regions of China will increase when the global temperature increases by 1.5/2.0 °C. The yearly maximum five-day precipitation (RX5D) averaged over China is reported to increase by 4.4% via the CMA-RegCM under the 1.5 °C warming in comparison with the baseline period (1986-2005); however, a relatively large increase of 11.1% is reported by the multi-model ensemble median (MME) when using the other five models. Furthermore, the reoccurring risks of precipitation extremes over most regions of China will further increase due to the additional 0.5 °C warming. For example, RX5D will further increase by approximately 8.9% over NWC, 3.8% over NC, 2.3% over SC, and approximately 1.0% over China. Extremes, such as the historical 20-year return period event of yearly maximum one-day precipitation (RX1D) and RX5D, will become more frequent, with occurrences happening once every 8.8 years (RX1D) and 11.5 years (RX5D) under the 1.5 °C warming target, and there will be two fewer years due to the additional 0.5 °C warming. In addition, the intensity of these events will increase by approximately 9.2% (8.5%) under the 1.5 °C warming target and 12.6% (11.0%) under the 2.0 °C warming target for RX1D (RX5D).

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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.

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With the increasing risk of meteorological disasters, it is of great importance to analyze the spatiotemporal changes of precipitation extremes and its possible impact on rice productivity, especially in Jiangsu province, southeast China. In this study, we explored the relationships between rice yield and extreme precipitation indices using Mann-Kendall trend test, Pettitt's test, and K-means clustering methods. This study used 10 extreme precipitation indices of the rice growing season (May to October) based on the daily precipitation records and rice yield data at 52 meteorological stations during 1961-2012 in Jiangsu province. The main findings were as follows: (1) correlation results indicated that precipitation extremes occurred in the months of July, August, and October, which had noticeable adverse effects on rice yield; (2) the maximum 7-day precipitation of July and the number of rainy days of August and October should be considered as three key indicators for the precipitation-induced rice meteorological disasters; and (3) most of the stations showed an increasing trends for the maximum 7-day precipitation of July and the number of rainy days of August, while the number of rainy days of October in all the stations demonstrated a decreasing trend. Moreover, Jiangsu province could be divided into two major sub-regions such as north and south areas with different temporal variations in the three key indicators.

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Precipitation extremes play a key role in flooding risks over the Huaihe River Basin, which is important to understand their hydrological impacts. Based on observed daily precipitation and streamflow data from 1958 to 2009, eight precipitation indices and three streamflow indices were calculated for the study of hydrological impacts of precipitation extremes. The results indicate that the wet condition intensified in the summer wet season and the drought condition was getting worse in the autumn dry season in the later years of the past 50 years. The river basin had experienced higher heavy rainfall-related flooding risks in summer and more severe drought in autumn in the later of the period. The extreme precipitation events or consecutive heavy rain day events led to the substantial increases in streamflow extremes, which are the main causes of frequent floods in the Huaihe River Basin. The large inter-annual variation of precipitation anomalies in the upper and central Huaihe River Basin are the major contributor for the regional frequent floods and droughts.

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

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