Increase In Extreme Precipitation Research Articles

Detection and Attribution of Changes in Precipitation Extremes in China and Its Different Climate Zones

Abstract Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.

Spatial Variation of Changes in Extreme Discharge Seasonality Across the Northeastern United States

ABSTRACTThe Northeast United States exhibits significant spatial heterogeneity in flood seasonality, with spring snowmelt‐driven floods historically dominating northern areas, while other regions show more varied flood seasonality. While it is well documented that since 1996 there has been a marked increase in extreme precipitation across this region, the response of flood seasonality to these changes in extreme precipitation and the spatial distribution of these effects remain uncertain. Here we show that, historically, snowmelt‐dominated northern regions were relatively insensitive to changes in extreme precipitation. However, with climate warming, the dominance of snowmelt floods is decreasing and thus the extreme flood regimes in northern regions are increasingly susceptible to changes in extreme precipitation. While extreme precipitation increased everywhere in the Northeastern United States in 1996, it has since returned to near pre‐1996 levels in the coastal north while remaining elevated in the inland north. Thus, the inland north region has and continues to experience the greatest changes in extreme flooding seasonality, including a substantial rise in floods outside the historical spring flood season, particularly in smaller watersheds. Further analysis reveals that while early winter floods are increasingly common, the magnitude of cold season floods (Nov‐May) have remained unchanged over time. In contrast, warm season floods (June‐Oct), historically less significant, are now increasing in both frequency and magnitude in the inland north. Our results highlight that treating the entire Northeast as a uniform hydroclimatic region conceals significant regional variations in extreme discharge trends and, more generally, climate warming will likely increase the sensitivity of historically snowmelt dominated watersheds to extreme precipitation. Understanding this spatial variability in increased extreme precipitation and increased sensitivity to extreme precipitation is crucial for enhancing disaster preparedness and refining water management strategies in affected regions.

Quantitative analysis of the sensitivity and spatial stratified heterogeneity of extreme precipitation across river basins

Global warming is expected to lead to a continuous increase in extreme precipitation. However, the response of extreme precipitation to climate change remains not entirely clear. This study quantitatively analyzes extreme precipitation in multiple river basins of China over the past nearly 60 years using high spatial resolution data. Both extreme precipitation and mean precipitation exhibit a spatial distribution pattern of higher values in the southeast and lower values in the northwest. However, their sensitivity to temperature shows an asymmetric spatial pattern, with higher sensitivity in the northwest and lower sensitivity in the southeast. Overall, the sensitivity of extreme precipitation to temperature (9.6 %/K) is 1.1 times that of mean precipitation (8.8 %/K). Additionally, the sensitivity of extreme precipitation in relatively dry areas (Continental River Basin) is about five times that in relatively wet areas (other river basins). Vegetation has the highest explanatory power (i.e., q-value) for the spatial stratified heterogeneity of extreme precipitation, ranging from 0.61 to 0.72, with the explanatory power of natural factors exceeding that of societal factors. Furthermore, the interaction between vegetation and elevation enhances the explanatory power for the spatial stratified heterogeneity of extreme precipitation, reaching values between 0.74 and 0.82.

The Relationship between Extreme Precipitation and Damaging Floods in the Northeastern United States

Abstract The northeastern United States has experienced an increase in extreme precipitation over the last three decades, which has substantial implications for flooding. However, the relationship between extreme precipitation and societally relevant flooding in the Northeast is not well understood. Here, we investigate the association between precipitation, antecedent wetness, and damage-producing warm-season (June–November) flood reports in the Northeast, 1996–2020. We employ 4-km gridded daily precipitation data aggregated to the county scale to calculate 1- and 3-day precipitation and a standardized precipitation index at multiple time scales. From these, we determine extreme precipitation and extreme antecedent wetness thresholds to identify hydroclimatic extreme events and relate them to damaging flood occurrences from the NOAA Storm Events Database. We find that damaging floods frequently occur with relatively high precipitation and antecedent wetness, although there are exceptions in several counties. However, the majority (54%) of damaging floods are not associated with extreme precipitation or extreme antecedent wetness, and more than 90% of hydroclimatic extreme events are not associated with floods. We explore limitations to the definitions of extreme precipitation and extreme antecedent wetness by varying the threshold. Lower hydroclimatic extreme event thresholds not only have a greater likelihood of being associated with damaging floods but also have a greater number of hydroclimatic extreme events not associated with a damaging flood; the opposite holds for higher thresholds. We address these challenges by combining precipitation and antecedent wetness to identify coordinated thresholds that best capture damaging flooding in each county.

Examining the evolution of extreme precipitation event using reanalysis and the observed datasets along the Western Ghats

AbstractIn recent decades, India has witnessed an increase in the intensity, frequency, and spread of extreme weather events. The widespread increase in extreme precipitation over the Western Coast of India is a matter of great concern. The factors contributing to such devastating extreme precipitation remain unclear due to the variability present in meteorological and oceanic variables and associated large‐scale circulations. Using reanalysis and observed datasets, we attempted to identify a combination of dynamic, thermodynamic, and cloud microphysics processes contributing to the anomalous precipitation from August 1 to 10, 2019 against its climatology. Our key findings highlight the crucial role of warm sea surface temperatures (anomaly >1.4°C), outgoing longwave radiation (anomaly <−50 W·m−2), and atmospheric temperature (anomaly over the ocean is >1.6°C) in enhancing the moisture‐holding capacity of the atmosphere by almost 10%. This elevated moisture, propelled by intensified low‐level winds (anomalies exceeding 4 m·s−1), leads to a shift from ocean to land. Notably, we observe that vertical updrafts (anomalies >−0.4 m·s−1) contribute to increased atmospheric instability and moisture convergence. The presence of an ample amount of cloud hydrometeors, with anomalies surpassing 2.5 × 10−4 kg·kg−1, establishes conditions conducive to sustained intense precipitation. Our findings deepen our understanding of the complex relationships between ocean and atmospheric dynamics, and wind patterns, and emphasize their pivotal influence on regional weather patterns and land surface hydrology.

Relationship between interannual variability of the north edge of the East Asian summer monsoon and extreme precipitation in North China

The north edge of the East Asian summer monsoon (EASM) is defined by using the isoline with average total column water vapor (TCWV) equal to 25 mm, and the relationship between the north edge of the EASM and summer extreme precipitation in North China (NC) from 1961 to 2020 is discussed. Self-Organizing Maps (SOM) method is used to cluster extreme precipitation in the abnormal years of the north edge in NC, and the circulation characteristics of different types of extreme precipitation are discussed. The results show that the north edge of the EASM is closely related to extreme precipitation in NC. When the north edge is northerly, both the frequency and total amount of extreme precipitation increase significantly in the monsoon marginal zone. Based on different monsoon circulation background, summer extreme precipitation events in NC are divided into four categories, namely, the central type and southern type in the northerly years of north edge, and the central type and eastern type in the southerly years of north edge. The occurrence of different types of extreme precipitation is closely related to the different characteristics of local trough, the western Pacific subtropical high (WPSH), and wave trains in the middle and high latitudes. Specifically, the central type extreme precipitation is affected by the mid-latitude wave train in the northerly years of north edge. As for the central type extreme precipitation in the southerly years of north edge, both high-latitude and mid-latitude wave trains transfer from west to east and converge over NC. However, the wave disturbance energy is relatively weak, leading to the extreme precipitation with weak intensity.

Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP

The southeastern region of Nigeria is susceptible to flood disasters primarily triggered by extreme precipitation with localized impacts. This study uses the output of High-Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate seasonal dependent changes in precipitation extremes in the near future (2031–2050) in the Anambra-Imo River Basin, in the southeastern region of Nigeria. Evaluating the models against observation for the 1995–2014 period, it is found that models reproduced the spatial pattern of the observed annual precipitation extremes over the river basin. Results show that in the near future, annual precipitation extremes will be characterized by a robust increase in annual total precipitation amount (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation (R10mm). The models project a significant increase in PRCPTOT, RX5day, R10mm, and wet-day intensity (SDII) for the June–July–August (JJA) and September–October–November (SON) seasons. The results demonstrate a robust and higher magnitude increase in precipitation extremes during the SON season. Specifically, PRCPTOT, RX5day, R10mm and SDII are projected to increase by up to 46 mm, 24 mm, 1.2 days and 2.4 mm/day, respectively. Whereas during the March–April-May (MAM) season, the HighResMIP suggests that PRCPTOT, R10mm, and SDII will marginally increase over the eastern part of the Anambra-Imo River Basin. Besides, the December–January–February (DJF) season will be characterized by a marginal increase in the precipitation extremes, especially over the southern fringes of the river basin. We note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes during the JJA and SON, potentially exacerbating flash flooding in the river basin. Hence, the results of this study may be vital for near-term socio-economic planning and policy decisions that will minimize the impact of flood disasters in the Anambra-Imo River Basin.

Land–atmosphere feedbacks weaken the risks of precipitation extremes over Australia in a warming climate

The importance of land–atmosphere feedbacks on regional precipitation changes has been recently noted. However, how land–atmosphere feedbacks shape daily precipitation distributions, particularly the tails of precipitation distributions associated with extreme events, remains unclear on a regional scale. Herein, using the latest land–atmosphere coupling experiments, this study reveals a consistent weakening effect of land–atmosphere feedbacks on the future increase in precipitation extremes over Australia, revealing the most pronounced reduction (56.8%) for the long-term (2080–2099) projection under the low emission (SSP1-2.6) scenario. This weakening effect holds true for shifts in the extreme tail of precipitation distribution, resulting in a reduced risk of precipitation extremes in a warming climate. Land‒atmosphere feedbacks offset 28%–60% of the occurrence risk for the 99th percentile of daily precipitation, with the largest reduction of 172% when precipitation exceeds the 99.7th percentile in the long-term projection under the high emission (SSP5-8.5) scenario. Considering less water replenishment, these feedbacks may reduce the risk of flooding but potentially expedite droughts, highlighting the role of land–atmosphere feedbacks in extreme event projection and regional climate adaptation.

Future Changes of Extreme Precipitation and Related Atmospheric Conditions in East Asia under Global Warming Projected in Large Ensemble Climate Prediction Data

Abstract Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes of extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the TRMM and ERA5 datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relatively humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future, because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.

Intensified response of extreme precipitation to rising temperature over the Tibetan Plateau from CMIP6 multi-model ensembles

This study conducted bias correction of the high-performing general circulation model (GCM) data in combination with observed data based on the latest GCM data released by the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ability of GCMs to simulate the characteristics of extreme precipitation over the Tibetan Plateau (TP) was systematically assessed, and the CMIP6 multi-model ensemble was used to accurately project extreme precipitation. Furthermore, the response of extreme precipitation over the TP to global warming was explored. The results demonstrated that daily translation efficiently addressed the issue of apparent overestimation in the model’s raw output, and CMIP6 multi-model ensemble mean exhibited better simulation performance for all extreme precipitation indices over the TP. Additionally, the simulation performance of each index exhibited a divergence across various elevation bands as altitude increased, with the best and worst performance observed at the altitude bands of 1500–2000 m and above 5000 m, respectively. For future changes in extreme precipitation, the indices representing the amount and intensity of extreme precipitation increase significantly, whereas the indices representing the duration of precipitation decrease. Moreover, the plateau will show a spatial pattern of “wet regions becoming dry and dry regions becoming wet”. The relationship between extreme precipitation and temperature follows a positive and peak pattern over most areas of the plateau, while the response of extreme precipitation to temperature will increase by 0.6 %–52.6 % with rising temperature. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

The increases in extreme climatic events over the northeastern Tibetan Plateau and their association with atmospheric circulation changes

In the context of global warming, increasing frequency of climate extremes poses a great challenge to natural systems and humankind. However, the spatial and temporal characteristics, as well as the mechanisms of extreme climate change, remain insufficiently understood, particularly in arid and semi-arid mountainous regions with high climate sensitivity and ecological fragility. In this study, we analyzed the spatial and temporal characteristics of 27 extreme climate indices (including intensity, duration, and frequency) in the Qilian Mountains, northeastern Tibetan Plateau, from 1961 to 2016 using the CN05.1 meteorological grid dataset, explored the relationship between the extreme climate indices and internal variability of the climate system (e.g., the North Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Arctic Oscillation (AO)), and further probed into the mechanisms affecting climate extremes using ERA5 reanalysis dataset. The results showed that extreme temperatures in the Qilian Mountains were significantly enhanced in terms of intensity, frequency, and duration during 1961–2016. The decrease rates of frost days (FD0) and ice days (ID0) were − 4.4 d per decade and − 4.7 d per decade, respectively, and the increase rate of growing season length (GSL) was 2.9 d per decade. Extreme precipitation intensity increased significantly, with the intensity indices R95P and PRCPTOT showing the most significant change trends of 4 mm per decade and 14.9 mm per decade, respectively (p < 0.05). The number of consecutive dry days (CDD) displayed a significant downward trend, with a rate of −6 d per decade, the number of moderate precipitation days (R10) showed an increasing trend of 0.12 d per decade. Correlation analyses revealed significant associations between the increases in extreme climates and the AMO and PDO. Additionally, the enhancement of the AO contributed to the increased extreme precipitation. Enhanced anticyclonic circulation in Eurasia and geopotential height, as well as increased cloud cover in winter, were the main reasons for the increase in extreme temperatures in the Qilian Mountains, whereas the significant increase in water vapor transport entering the east boundary of the Qilian Mountains in summer mainly contributed to the increase in extreme precipitation. This study highlights internal oscillations in the climate system that regulate the extreme climate variability in the Qilian Mountains at various spatial-temporal scales.

Increases in extreme precipitation expected in Northeast China under continued global warming

Increases in extreme precipitation expected in Northeast China under continued global warming

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

The effect of nitrogen input on N2O emission depends on precipitation in a temperate desert steppe

Nitrous oxide (N2O) is the third most important greenhouse gas, and can damage the atmospheric ozone layer, with associated threats to terrestrial ecosystems. However, to date it is unclear how extreme precipitation and nitrogen (N) input will affect N2O emissions in temperate desert steppe ecosystems. Therefore, we conducted an in-situ in a temperate desert steppe in the northwest of Inner Mongolia, China between 2018 and 2021, in which N inputs were combined with natural extreme precipitation events, with the aim of better understanding the mechanism of any interactive effects on N2O emission. The study result showed that N2O emission in this desert steppe was relatively small and did not show significant seasonal change. The annual N2O emission increased in a non-linear trend with increasing N input, with a much greater effect of N input in a wet year (2019) than in a dry year (2021). This was mainly due to the fact that the boost effect of high N input (on June 17th 2019) on N2O emission was greatly amplified by nearly 17–46 times by an extreme precipitation event on June 24th 2019. In contrast, this greatly promoting effect of high N input on N2O emission was not observed on September 26th 2019 by a similar extreme precipitation event. Further analysis showed that soil NH4+-N content and the abundance of ammonia oxidizing bacteria (amoA (AOB)) were the most critical factors affecting N2O emission. Soil moisture played an important indirect role in regulating N2O emission, mainly by influencing the abundance of amoA (AOB) and de-nitrification functional microorganisms (nosZ gene). In conclusion, the effect of extreme precipitation events on N2O emission was greatly increased by high N input. Furthermore, in this desert steppe, annual N2O flux is co-managed through soil nitrification substrate concentration (NH4+-N), the abundance of soil N transformation functional microorganisms and soil moisture. Overall, it was worth noting that an increase in extreme precipitation coupled with increasing N input may significantly increase future N2O emissions from desert steppes.

Increased population exposures to extreme precipitation in Central Asia under 1.5 ℃ and 2 ℃ global warming scenarios

The increase in extreme precipitation (EP) may pose a serious threat to the health and safety of population in arid and semi-arid regions. The current research on the impact of EP on population in Central Asia (CA) is insufficient and there is an urgent need for a comprehensive assessment. Hence, we opted for precipitation and temperature data under two Shared Socioeconomic Pathways (SSP2–4.5 and SSP5–8.5) from ten Global Climate Models (GCMs), which were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). By integrating population data in 2020 and 2050 (SSP2 and SSP5), we investigated the future changes in EP and population exposure in CA under 1.5 °C and 2 °C global warming scenarios (GWSs). Our analysis indicates that EP in CA is projected to increase with global warming. Under the SSP5–8.5, the maximum daily precipitation (Rx1day) exhibits an average response rate to global warming of 3.58 %/K (1.99–4.06 %/K). With rising temperatures, an increasing number of areas and populations in CA will be impacted by EP, especially in the Fergana valley. Approximately 25% of the population (land area) in CA is exposed to Rx1day with increases of more than 8.31% (9.32%) under 1.5 °C GWS and 14.18% (13.25%) under 2 °C GWS. Controlling temperature rise can be effective in reducing population exposures to EP. For instance, limiting the temperature increase to 1.5 °C instead of 2 °C results in a 2.79% (1.75%–4.59%) reduction in population exposure to Rx1day. Finally, we found that climate change serves as the predominant factor influencing the population exposure to EP, while the role of population redistribution, although relatively minor, should not be disregarded. Particularly for prolonged drought, the role of population redistribution manifests negatively.

Climate change signals of extreme precipitation return levels for Germany in a transient convection‐permitting simulation ensemble

AbstractThe increase in extreme precipitation with global warming (GW) and associated uncertainties are major challenges for climate adaptation. To project future extreme precipitation on different time and intensity scales (return periods [RPs] from 1 to 100 a and durations from 1 h to 3 days), we use a novel convection‐permitting (CP), multi‐global climate model ensemble of COSMO‐CLM regional simulations with a transient projection time (1971–2100) over Germany. We find an added value of the CP scale (2.8 km) with respect to the representation of hourly extreme precipitation intensities compared to the coarser scale with parametrized deep convection (7 km). In general, the return levels (RLs) calculated from the CP simulations are in better agreement with those of the conventional observation‐based risk products for the region for short event durations than for longer durations, where an overestimation by the simulation‐based results was found. A maximum climate change signal of 6–8.5% increase per degree of GW is projected within the CP ensemble, with the largest changes expected for short durations and long RPs. Analysis of the uncertainty in the climate change signal shows a substantial residual standard deviation of a linear approximation, highlighting the need for transient data sets instead of time‐slice experiments to increase confidence in the estimates. Furthermore, the ensemble spread is found to be smallest for intensities of short duration, where changes are expected to be based mainly on thermodynamic contributions. The ensemble spread is larger for long, multi‐day durations, where a stronger dependence on the dynamical component is ascribed. In addition, an increase in spatial variance of the RLs with GW implies a more variable future climate and points to an increasing importance of accounting for uncertainties.

Projected increase in widespread riverine floods in India under a warming climate

Widespread floods simultaneously affect several subbasins in a large river basin and pose severe challenges to disaster management and flood mitigation efforts. India is highly susceptible to widespread flooding as the country receives more than 70% of annual rainfall in the four-month period from June to September. However, the potential impacts of climate change on the drivers and frequency of widespread floods in India remain unexplored. We use observations, climate model simulations, and a hydrological model to examine the drivers and changes in widespread floods under a projected future climate. The observed increase in extreme precipitation does not translate to flooding in most river basins in India due to its fragmented nature. Translation of extreme precipitation to flood depends on the catchment area. We examined the changes in the widespread floods in Indian river basins using a hydrological model and climate projections. Robust increases in intense precipitation area and frequency are projected under the warming climate. Furthermore, hydrological model simulations indicate a profound increase in widespread floods in Indian river basins under a warming climate. The increase in widespread floods in a warming climate can pose challenges for flood mitigation and management in the future. Consideration of spatial extent of extreme precipitation and catchment area can enhance flood monitoring and prediction efforts. In addition, increases in extreme precipitation may not always lead to rise in flooding.

Spatial and Temporal Variability Characteristics and Driving Factors of Extreme Precipitation in the Wei River Basin

As global climate change intensifies, the global atmospheric circulation process is undergoing significant changes, and the local water vapor pattern has also changed. This study takes the Wei River Basin as the research area. Firstly, an evaluation index system for extreme precipitation was established, and the time-series characteristics of the magnitude, frequency, and duration of extreme precipitation were analyzed. Statistical methods were used to analyze the non-consistency in time-series changes in extreme precipitation indicators. Using spatial heterogeneity analysis methods, the spatial variation differences in extreme precipitation in the Wei River Basin were identified. This study selected the El Niño-Southern Oscillation (ENSO) index, global land-ocean temperature index (LOTI), and land surface temperature (LST) index to quantitatively evaluate the impact of climate change on regional extreme precipitation and analyzed the correlation between temperature and extreme precipitation, identifying the key driving factors of extreme precipitation changes. The conclusions of this study are as follows: (1) The southern region of the Wei River Basin experiences more frequent and intense precipitation events, while the northern region experiences relatively few. (2) From 1981 to 2021, the intensity, frequency, and duration of precipitation events in the Wei River Basin gradually increased, with the most significant increase in extreme precipitation in the Guanzhong Plain. (3) Global climate change has an important impact on precipitation events in the Wei River Basin. The increase in the ENSO, LOTI, and LST indices may indicate an increase in the probability of drought and flood events in the Wei River Basin. The relationships between extreme precipitation and temperature present a peak structure. This conclusion is helpful to better understand the impact of climate change on extreme precipitation in the Wei River Basin and provides some support for the response to extreme meteorological events under the background of future climate change.

The increase in extreme precipitation and its proportion over global land

Changes in extreme precipitation (EP) have a significant impact on the ecology and sustainable development of most regions, especially when considered against a backdrop of global warming. Using trend analysis and other methods, we analyze the changes in nine extreme precipitation indices (EPIs), looking at the proportion of EP in total precipitation from the perspectives of intensity, frequency, and duration. We also utilize a geo-detector method to investigate the impacts of 15 climate factors on EPIs. Our results indicate that although EP and their proportions show an overall increasing trend on a global scale, they had undergone a process of initial decrease followed by increase over the past approximately 70 years. And this overall global increases are charged by the significant increases happened in Northern Hemisphere and western of South America, especially EP intensity and frequency indices, and their proportion. Whereas CDD shows an opposite trend in spatial distribution, the CWD is decreasing globally. The EPIs and their proportions are all highest in the low elevation belt (≤2000 m), followed by the high elevation belt (≥4000 m), and lowest in the mid-elevation belts (2000-4000 m). Most of them have returned to or exceeded the levels of the mid-20th century in all three elevation belts. Only CWD shows a decreasing trend, which indicates that more EP amounts are occurring with less precipitation duration. In some arid areas, this makes it possible that a short period of precipitation may complete the total amount of precipitation for that year. Global temperature is proven to be the most important factor influencing the EPI globally, and followed by the temperature of each region. And the regions of high latitudes of the Northern Hemisphere and Antarctica which are greatly influenced by global temperature demonstrate the Polar amplification effect. The East Atlantic-West Russia Pattern, Atlantic Multi-Decadal Oscillation, and El Nino-Southern Oscillation indices are the secondly important factors leading to changes in the proportion of global EP. At the same time, we also find that the influence of non-monsoon factors on EP is gradually strengthening. This study clarifies changes in EP globally and in various regions, which will help provide a scientific reference for preventing disasters caused by extreme climate change.