Future Extreme Precipitation Research Articles

Constrained Precipitation Extremes Reveal Unequal Future Socioeconomic Exposure

AbstractExtreme precipitation can lead to major flooding, impacting human health and safety. Thus, reliable projections of population and GDP exposure to future extreme precipitation are imperative. Here, we quantify future precipitation characteristics from robust emergent constraint relationships between historical and future monthly precipitation extremes (99th percentile) across 19 CMIP6 models (r2 > 0.7 in 74–84% of 0.5° global land grids), and narrow uncertainty by 37.0–39.5% (absolute reduction being 0.753–0.774 mm/day). The constrained grid‐averaged future 99th percentile extreme is 6.96 ± 0.0059, 7.03 ± 0.0061, 7.11 ± 0.0063, and 7.29 ± 0.0067 mm/day, under SSP126, SSP245, SSP370, and SSP585, respectively, which exceeds historical extremes substantially in terms of intensity (12.9–19.7%) and frequency (1.6–2.3 times more). Future population and GDP exposed to 99th percentile extreme precipitation grow quickly, and are projected to exceed 1 million people in 27–40 countries and 10 billion USD (2005 Purchasing‐Power Parity) in 48–77 countries. Growth of future population exposure is dominated by an increase in extreme precipitation frequency rather than a rise in population, especially in developed countries. GDP exposure is controlled by the coupled effects of rapid socio‐economic development and significant shifts in precipitation frequency. Using indices of socio‐economic vulnerability, government effectiveness and economic freedom, we identify the unequal situation that high‐risk countries with high exposure are commonly characterized by low GDP per capita and high sociopolitical instability.

Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China

The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management.

Ancient wisdom: A new perspective on the past and future Chinese precipitation patterns based on the twenty-four solar terms

Precipitation, as a crucial component of the hydrological cycle, exhibits intense responsiveness to climatic changes. Accurately and finely describing precipitation patterns on a temporal scale within complex climatic regions is a matter that has consistently garnered attention. China, with its intricate geographical environment spanning five climatic zones, has seen its laboring populace develop the unique Twenty-Four Solar Terms (TFST) calendar from ancient times to macroscopically describe climatic shifts and to guide their agricultural activities. Here, based on the chronological framework of the TFST, we revisited and validated the response of typical solar terms to conventional precipitation indicators over the past 40 years in China. By comparing the accumulated precipitation volume and the number of effective precipitation days 30 days before and after the typical solar terms, we established the statistical credibility of this ancient calendar system’s summarization of precipitation patterns. Furthermore, the study examines the response of various future extreme precipitation events to the TFST and their spatial distribution patterns. We identified that more than 70 % of regions in China experienced a concentration of extreme precipitation events during the XiaoShu and DaShu solar terms. Under four different future scenario models, there is prediction that over 55.2 % of the areas in China will also have extreme precipitation events predominantly during the same two solar terms. Additionally, in the Qinghai-Tibet region, there is a trend indicating an increased risk of extreme precipitation frequency during these two critical solar terms associated with rising carbon emissions, with a significance level averaging over 95 %. This research deeply integrates modern meteorological and hydrological science with traditional culture, allowing for a nuanced understanding of the periodicity of extreme precipitation. It also offers a novel perspective and theoretical basis to guide flood prevention efforts and studies on the regularities of extreme precipitation.

Projected urban flood risk assessment under climate change and urbanization based on an optimized multi-scale geographically weighted regression

Previous research has primarily focused on evaluating flood risks influenced by single factors, often neglecting the combined impacts of climate change and urbanization on historical and future flood risks. This study utilized 12 assessment indicators across three criteria layers. It employed the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and an optimized Multi-scale Geographically Weighted Regression (MGWR) to develop a comprehensive assessment framework for historical and projected urban flood risks under combined scenarios of RCPs and SSPs. The risk of future extreme precipitation events increased under 4 RCPs. Compared with 2010, Beijing's urban land area is expected to increase by an average of 53.64 % in 2060 under 5 SSPs, accompanied by rapid socioeconomic development. MGWR based on K-means optimization improves operational speed by reducing the number of patches. The areas classified as high and highest flood risk levels increased by 38.87 % and 60.17 % from the 2010s to the 2060s, indicating an upward trend in future flood risk. Higher flood risk is observed under the high emissions combination scenario. Structural equation modeling (SEM) revealed that road network density (RND), runoff depth (RD), and distance to road (DRO) were the main influencing factors contributing to flood risks in Beijing.

Projection of extreme climate change in the Asian arid region and the Tibetan Plateau in the early and middle 21st century based on NEX-GDDP-CMIP6

In the context of global warming, it is anticipated that both the intensity and the frequency of future global extreme high precipitation (EHP) and extreme high temperature (EHT) events will increase. To evaluate the future extreme climate changes in the Asian arid region and Tibetan Plateau, this study applied the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) to assess the changes in EHP (Rx5d and R95pTOT) and EHT (TX90p and TXx) under different emission scenarios in the 21st century. Findings suggest that both the frequency and the intensity of the extreme indices will increase, exhibiting accelerated growth under higher emission scenarios, particularly under the SSP5-8.5 emission scenario. It is suggested that the growth of EHT and EHP in the early subperiod of the 21st century (2026–2045) will be relatively moderate, with small differences between different emission scenarios. However, by the middle subperiod of the 21st century (2041–2060), the differences between different emission scenarios will become larger than the 2035s and the growth will become more intense. In western central Asia, TX90p, TXx, Rx5d, and R95pTOT increase by 9.7%–14.2% (13.3%–24.7%), 1.3°C–1.7°C (1.6°C–2.7°C), 6.5%–8.9% (8.2%–8.8%), and 18.1%–27.0% (25.6%–30.0%) by the early (middle) subperiod; in eastern central Asia, TX90p, TXx, Rx5d, and R95pTOT increase 8.1%–12.0% (11.3%–21.1%), 1.4°C–1.8°C (1.9°C–2.9°C), 7.4%–9.7% (10.4%–13.8%), and 20.2%–29.3% (32.0%–40.8%) by the early (middle) subperiod; and over the Tibetan Plateau, TX90p, TXx, Rx5d, and R95pTOT increase 12.5%–17.4% (17.0%–31.0%), 1.2°C–1.5°C (1.6°C–2.5°C), 7.2%–10.0% (9.9%–15.0%), and 26.6%–33.1% (36.1%–55.3%) by the early (middle) subperiod.摘要近年来, 随着全球变暖, 大部分地区极端气候事件的风险都有所增加. 作为中国一带一路建设核心区域的亚洲干旱区和青藏高原区域面临着严峻的极端气候风险. 本文利用NEX-GDDP-CMIP6数据预估了未来21世纪早期 (2026–2045) 和中期 (2041–2060) 不同排放情景下亚洲干旱区和青藏高原相较气候参考期 (1995–2014) 极端高温指数和极端降水指数的变化. 预估结果显示, 在不同排放情景下极端高温指数和极端降水指数都有所上升, 且在SSP5-8.5情景下增长最多, 其中NEX-GDDP-CMIP6不同模式对极端高温指数的模拟一致性高于极端降水指数. 21世纪中期相比早期极端高温指数和极端降水指数的增长更加严重.

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

An assessment of the projected changes in precipitation extremes for the 21st century is presented here for Greece and its individual administrative regions. The analysis relies on an ensemble of high-resolution Regional Climate Model (RCM) simulations following various Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The simulated changes in future annual total precipitation (PRTOT) under the examined scenarios are generally negative but statistically non-robust, except towards the end of the century (2071–2100) over high-altitude mountainous regions in Western Greece, Peloponnese, and Crete under RCP8.5. The pattern of change in the number of very heavy precipitation days (R20) is linked to the respective pattern of the PRTOT change with a statistically robust decrease of up to −5 days per year only over parts of the high-altitude mountainous regions in Western Greece, Peloponnese, and Crete for 2071–2100 under RCP8.5. Contrasting the future tendency for decrease in total precipitation and R20, the changes in the intensity of precipitation extremes show a tendency for intensification. However, these change patterns are non-robust for all periods and scenarios. Statistical significance is indicated for the highest 1-day precipitation amount in a year (Rx1day) for the administrative regions of Thessaly, Central Greece, Ionian Islands, and North Aegean under RCP8.5 in 2071–2100. The changes in the contribution of the wettest day per year to the annual total precipitation (RxTratio) are mainly positive but non-robust for most of Greece and all scenarios in the period 2021–2050, becoming more positive and robust in 2071–2100 for RCP8.5. This work highlights the necessity of taking into consideration high-resolution multi-model RCM estimates in future precipitation extremes with various scenarios, for assessing their potential impact on flood episodes and the strategic planning of structure resilience at national and regional level under the anticipated human-induced future climate change.

Influence of teleconnections on observations and projections of hydroclimatic extremes caused by tropical cyclones in the arid climate of Baja California Sur

This study focuses on identifying modulations by large-scale synoptic, inter-annual, and decadal oscillations on the extreme rainfall in the state of Baja California Sur, and provides statistical models to forecast future evolution. The region is arid, with 70% of precipitation from July to October, and is affected by tropical systems that may lead to moderate and even intense precipitation. Seven clusters were obtained using the Ward method applied to quality-controlled climatological data from 1950 to 2014. Normalized extreme precipitation (95th percentile) shows an overall increase in the last decades (1995-2004 and 2005-2014), with total values much larger than in any of the previous 50 years. Multivariate linear models (MLMs) were developed based on indices for the Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO) in Region 3.4, which were shown to modulate extreme precipitation. The MLM based on PDO, ENSO, and the fraction of tropical cyclones (TC) within a radius of 300 km to the peninsula (M4), has a better correlation with observed rainfall than the historical simulations of the Coupled-Model Inter-comparison Project version 5 (CMIP5) models; moreover, M4 outperforms all other MLMs in six of the seven clusters. Projections were evaluated based on the MLMs and CMIP5 simulations under scenarios RCP4.5 and RCP8.5 for mid- and long-term horizons. Model M4 projects more extreme events than CMIP5, and all MLM projects negative trends in extreme precipitation from 2041 to 2100 under RCP8.5. This study provides valuable information on future extreme precipitation in an arid region in the presence of steep topography, which could result in potential damage to ecosystems and infrastructure.

Evaluation and projection of extreme precipitation using CMIP6 model simulations in the Yellow River Basin

Abstract The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin.

Spatial–temporal assessment of future extreme precipitation and extreme high‐temperature exposure across China

AbstractThis study utilizes climate scenario data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and population gridded data from shared socioeconomic pathways (SSPs) to identify extreme precipitation and high‐temperature events, along with their impact on the population in China and its subregions for both the near‐term future (2020–2050) and the long‐term future (2070–2100). The precipitation and temperature extremes in China are expected to increase during 2020–2100, and they will increase continuously with increasing radiative forcing. The spatial pattern of increases is similar across all SSPs‐RCPs scenarios, with a larger rise in the southern study area. Population exposure to extreme precipitation in China is anticipated to rise from 2020 to 2050 and decline from 2070 to 2100 under all scenarios, with a more pronounced decrease in SSP4‐6.0, and there is a transmutation in the chief determinant of populace vulnerability to extreme precipitation, transitioning from factors contingent upon population to those contingent upon climate from 2020–2050 to 2070–2100. In addition, temperature exhibits a general increasing trend in the impact area and population exposure from 2020–2050 to 2070–2100, concentrated in eastern and southern China. The exposed population's high‐value areas will continually expand with rising radiation forcing. Factors influencing population exposure to extremely high temperatures from 2020 to 2100, including climate, population, and their interaction, exhibit stable contribution rates, with population remaining the dominant factor.

Assessing urban drainage pressure and impacts of future climate change based on shared socioeconomic pathways

The increasing frequency of urban flood disasters presents a significant obstacle to urban sustainability. Urban flood management aims to reduce the flood occurrences, currently addressed through urban drainage systems. Previous studies have demonstrated future precipitation extremes will pose larger pressure on urban drainage network, but when and where the pressure will reach a dangerous level have never been assessed in any city of China. This study establishes the initial framework for identifying critical decades and hot spots of urban drainage pressure changes due to future climate change, through a case study conducted in southern China (Haining city). Urban drainage pressure was assessed by a combination of the urban drainage model known as the Storm Water Management Model (SWMM) and pipe statistics. Using climate projections from the latest phase of Coupled Model Intercomparison Project (CMIP6) under four typical SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios, we project the changes in urban drainage pressure by 21st century, and identify the key decades and high risk areas with the occurrence of dangerous pressure levels. The results indicate an overall upward trend in urban drainage pressure for Haining city, with over 97% of the flooding nodes projected to firstly reach the dangerous level by the 2030 s. Comparisons of the patterns under different SSP-RCP scenarios, suggest that a higher forcing pathway would expedite the deterioration of urban drainage pressure, particularly in urban areas with lower DEM and high building intensity. This has broad implications for better informing disaster management and policy-making in similar cities, especially those with inadequate drainage capacities.

Future changes in the influence of the NAO on Mediterranean winter precipitation extremes in the EC-Earth3 large Ensemble: The prominent role of internal variability

One of the largest uncertainties in future climate projections is the interplay between internally generated and externally forced changes. This study investigates the changes in the link between the North Atlantic Oscillation (NAO) and Mediterranean winter extreme rainfall and dry days by the end of the 21st century compared to present day. We compare two different future pathways and estimate the extent to which the NAO imprint is affected by the global warming level using the latest EC-Earth3 large ensemble historical and future experiments. It is shown that the expected range of winter extremes changes due to internal and unpredictable fluctuations of the NAO largely overcomes the signal associated with externally-forced NAO variations. The NAO is found to exert a similar control on European climate variability, regardless of the amount of warming. For most of the Mediterranean region, magnitude and even sign of projected changes in the NAO-congruent precipitation indices vary substantially across the individual ensemble members according to the corresponding evolution of the NAO. Internal variability provides an average basin-wide contribution of up to 90% or more to the total NAO-driven variability in SSP1–1.9, and of about 80% in SSP5–8.5. Sub-regionally, the anthropogenic component of the NAO link is more evident over the Iberian Peninsula and parts of the central Mediterranean. This emphasises the role of internal variability and related uncertainty in determining the future impact of the NAO via the large spread in the circulation responses. However, the NAO is found to exert a weaker influence on the extreme precipitation total variability in both future scenarios given their future marked increase in total intensity and variance as opposed to the negligible NAO-related trends. Opposite conclusions are drawn for dry days, which are projected to decrease in the future, especially in the northern Mediterranean. Thus, this study also highlights how the variability of future extreme precipitation intensity in the Mediterranean basin will be less dependent on the principal mode of internal climate variability, posing further challenges for prediction and adaptation to weather-related hazards.

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.

Predicting extreme sub-hourly precipitation intensification based on temperature shifts

Abstract. Extreme sub-hourly precipitation, typically convective in nature, is capable of triggering natural disasters such as floods and debris flows. A key component of climate change adaptation and resilience is quantifying the likelihood that sub-hourly extreme precipitation will exceed historical levels in future climate scenarios. Despite this, current approaches to estimating future sub-hourly extreme precipitation return levels are deemed insufficient. The reason for this can be attributed to two factors: there is limited availability of data from convection-permitting climate models (capable of simulating sub-hourly precipitation adequately) and the statistical methods we use to extrapolate extreme precipitation return levels do not capture the physics governing global warming. We present a novel physical-based statistical method for estimating the extreme sub-hourly precipitation return levels. The proposed model, named TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX), is based on a parsimonious non-stationary and non-asymptotic theoretical framework that incorporates temperature as a covariate in a physically consistent manner. We first explain the theory and present the TENAX model. Using data from several stations in Switzerland as a case study, we demonstrate the model's ability to reproduce sub-hourly precipitation return levels and some observed properties of extreme precipitation. We then illustrate how the model can be utilized to project changes in extreme sub-hourly precipitation in a future warmer climate only based on climate model projections of temperatures during wet days and on foreseen changes in precipitation frequency. We conclude by discussing the uncertainties associated with the model, its limitations, and its advantages. With the TENAX model, one can project sub-hourly precipitation extremes at different return levels based on daily scale projections from climate models in any location globally where observations of sub-hourly precipitation data and near-surface air temperature are available.

Assessing Future Precipitation Patterns, Extremes and Variability in Major Nile Basin Cities: An Ensemble Approach with CORDEX CORE Regional Climate Models

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

Forecasting extreme precipitations by using polynomial regression

It is well known that the recent global warming intensifies the magnitude of rainfalls due to the increase in water content in the atmosphere. Therefore, the probability of exceeding the previously observed extreme precipitation values also increases with the experienced climate change, and forecasting extreme weather events is becoming more important. This paper presents a new polynomial regression approach and software (PolReg), where future extreme precipitations exceeding all previous observations are estimated for each month of year by using prediction bounds with a level of certainty at 95%. The presented method determines the degrees and coefficients of best-fitting polynomials for each precipitation station and forecasts the expected extreme value for each month of year by using the determined polynomials. The performance of the method is tested by removing and estimating a total of 792 highest observed monthly total precipitation values of 66 precipitation stations in Turkey (the highest observation for each month of year for each station). The results show that the proposed method and the provided software have a high performance and accuracy in estimating future precipitation extremes and might be applied in many disciplines dealing with forecasting probable extreme values.

Prediction of Clausius–Clapeyron Scaling of Daily Precipitation Extremes over China

Abstract Observations and model simulations have confirmed that the higher percentiles of precipitation intensity of all wet days have increased with the local daily dewpoint temperature at the Clausius–Clapeyron (C-C) rate of 7% °C−1 using the binning technique over China. It is uncertain whether the binning scaling rates will remain constant in the future climate. Here, we use daily precipitation and dewpoint temperature (DPT) over China from 20 CMIP6 models to examine and project the change of binning scaling of precipitation extremes. The results of the study suggest that the multimodel ensemble median (MEM) generally captures the spatial distribution of binning scaling rates and binning curves in different climate zones across China. The binning scaling rates have stability in the future but with a large increasing trend (more than 30%) in southeast China in the far future (2061–2100) under the SSP5-8.5 scenario. CMIP6 models project that both the peak of extreme precipitation and the temperature for the plateau mountain zone will increase in the far future under the SSP5-8.5 scenario, implying that the peak structure does not provide an upper limit for future precipitation extremes. Significance Statement The binning scaling rates describe the relationship between precipitation extremes and temperature based on their day-to-day variations. However, it is still unclear whether the binning scaling rates of daily precipitation extremes with daily temperature will change in the future. We use 20 CMIP6 models to project the scaling rates of precipitation extremes with dewpoint temperature over China in the future. The results show that the sensitivity of precipitation extremes to dewpoint temperature will generally be stable, except that southeast China shows an increasing trend of up to 30%. Moreover, the peak of extreme precipitation and the temperature will increase with warming in the plateau mountain zone. Our results have implications for precipitation prediction and risk assessment.

Understanding extreme precipitation scaling with temperature: insights from multi-spatiotemporal analysis in South Korea

With global warming, the intensification of extreme precipitation events is anticipated to follow an exponential growth pattern aligned with the Clausius–Clapeyron (CC) scaling rate (approximately 7% per degree Celsius). However, the regional-scale response of extreme precipitation shows significant variability, deviating from the expected CC rate. This deviation is likely caused by diverse weather patterns and local fluctuations in thermodynamic influences, resulting in differences across seasons and within the region of interest. In this study, we examine the spatial distribution of scaling relationships between extreme precipitation and temperature in South Korea, considering daily and sub-daily scales, both annually and seasonally. For a thorough analysis, we utilize multiple precipitation accumulation periods, temperatures, and different conditional quantiles. Our results reveal that, at the annual scale, most scaling patterns exhibit a peak-like structure, with significant variations in breakpoints observed across temperature variables and regions. However, the southern area presents a notable exception with a positive scaling pattern, particularly with the dew point temperature. At the seasonal scale, we observe more variability, with notable shifts occurring during the wet season across different temperatures and regions. Lastly, we explore the long-term historical changes in the peak value in extreme precipitation and find significant increases at high quantiles in the southern area of South Korea. It informs that the observed peak like pattern does not impose a potential upper limit for extreme precipitation. Overall, our findings emphasize the need for cautious interpretation of precipitation scaling within specific spatiotemporal contexts, which could provide a solid basis for better understanding future extreme precipitation events in a changing climate.

Threshold response of ecosystem water use efficiency to soil water in an alpine meadow

Ecosystem water use efficiency (WUE) is a coupled index of carbon (gross ecosystem productivity, GEP) and water fluxes (transpiration, Tr or evapotranspiration, ET), reflecting how ecosystem uses water efficiently to increase its carbon uptake. Though ecosystem WUE is generally considered to decrease with increasing precipitation levels, it remains elusive whether and how it nonlinearly responds to extreme water changes. Here, we performed a 5-year precipitation halving experiment in an alpine meadow, combined with extremely interannual precipitation fluctuations, to create a large range of soil water variations. Our results showed that WUETr and WUEET consistently showed a quadratic pattern in response to soil water. Such quadratic patterns were steadily held at different stages of growing seasons, with minor changes in the optimal water thresholds (25.0–28.4 %). Below the water threshold, more soil water stimulated GEP but reduced Tr and ET by lowering soil temperature, resulting in a positive response of ecosystem WUE to soil water. Above the threshold, soil water stimulated GEP less than Tr (ET), leading to a negative response of ecosystem WUE to soil water. However, biological processes, including plant cover and belowground biomass as well as vertical root biomass distribution, had less effect on ecosystem WUE. Overall, this work is among the first to reveal the nonlinearity and optimal water thresholds of ecosystem WUE across a broad range of soil water, suggesting that future extreme precipitation events will more frequently surpass the water threshold and differently change the coupling relationships of carbon and water fluxes in alpine grasslands.

Future Extreme Precipitation in Summer Will Become More Widespread in China Depending on Level of Warming

AbstractIn this study, CMIP6 and single model initial‐condition large ensemble (SMILE) simulations were applied to reveal possible changes in the spatial coverage (RSC) of summer extreme precipitation (≥99th percentile) in China. Four different approaches were applied to provide a comprehensive assessment of RSC changes. Results indicate that the trend of RSC in summer for the period 1961–2020 in China is underestimated by raw simulations of CMIP6 multi‐model ensembles. Further analyses suggest that the confidence in model simulations reflecting the observed change can be improved based on the two observation constraint approaches and SMILE‐based approach. In addition, the reliability of the spatial distribution can also be improved. Projection results indicate that the RSC of summer extreme precipitation increases consistently with the increment of global warming across the different approaches. Among them, the results based on observational constraints and the SMILE‐based approach show enhanced reliability and present larger RSC changes than by directly using CMIP6 ensembles. In conclusion, extreme precipitation in summer is expected to become more widespread in China as the level of global warming increases.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.