Precipitation Extremes In China Research Articles

Projected population exposure to precipitation extremes in China by the end of the twenty-first century: trends, change points, and spatial variability

Projected population exposure to precipitation extremes in China by the end of the twenty-first century: trends, change points, and spatial variability

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–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.

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

AbstractThe rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario.

Rainstorm variability is mainly influenced by the southwest monsoon in the Hanjiang River basin of China during 1985–2020

AbstractThe extreme precipitation in China is greatly influenced by the East Asian summer monsoon, which can be divided into two components: the southeast monsoon (SEM) and the southwest monsoon (SWM). The Hanjiang River basin (HJB), a representative monsoon‐influenced basin in China, is alternately influenced by the southwest monsoon and southeast monsoon throughout the year. However, the specific relationship between rainstorms and monsoons, and the relative contribution of different monsoons, remains unclear in the HJB. Using multiple datasets, this study analysed the variability of various rainstorm types in the HJB during 1985–2020, investigated their relationships with different monsoon indicators and assessed the contribution of monsoons on the rainstorm variance. The results reveal a nonsignificant increasing trend in rainstorms within the basin, the severe convective rainstorm days accounted for a high proportion of the total rainstorm days, and the spatiotemporal characteristics of different rainstorm types are different. The rainstorm indicators are significantly correlated with the monsoon within the HJB and are positively correlated with the SWM, particularly its intensity. The SEM exhibits a weak correlation with rainstorms and has limited explanation for the variance in rainstorms. The intensity of SWM within the HJB shows a significant positive correlation with all rainstorm indicators. It is also detected as the most important indicator for explaining the interannual variation in rainstorms, explaining 41.97% and 39.45% of the variance in daily rainstorm frequency and totals, which comprises more than half of the total explanatory portion. The explanation of circulation factors for the variance in daily rainstorms is higher than that for severe convective rainstorms. In addition to monsoons, the Pacific Decadal Oscillation and the Southern Oscillation Index also have a great contribution to the rainstorm variability in the HJB.

Determination of duration, threshold and spatiotemporal distribution of extreme continuous precipitation in nine major river basins in China

China is one of the countries most severely affected by extreme precipitation, and it is urgent to carry out relevant research on the spatiotemporal distribution of extreme precipitation in China. Compared with single-day precipitation events, heavy precipitation events often last for multiple days and have greater potential disaster impacts, such as floods and mudslides. Therefore, in this study, we focus on continuous precipitation (CP) events for analysis. In order to determine the most probable duration of continuous heavy precipitation events, we innovatively adopted two time-frequency variation methods, namely Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT). Then through Detrended Fluctuation Analysis (DFA) and multifractal DFA (MF-DFA) methods, extreme CP thresholds are extracted according to the fluctuation characteristics of the CP series. The results show that the average duration of heavy precipitation in the Pearl River Basin (PRB) area was the longest (up to 7.7 days), followed by the Continental Basin (CB) area (7.6 days). The Haihe River Basin (HRB) area had the shortest duration of heavy precipitation (5.0 days), followed by the Huaihe River Basin (HURB) area (5.9 days). The extreme CP threshold in the Southeast Basin (SEB) area was the largest (112.17 mm), followed by the PRB area (108.22 mm). The extreme CP threshold in the CB area was the smallest (20.17 mm), followed by the Yellow River Basin (YRB) area (44.31 mm). Through mutation and trend testing to evaluate the changing state of the extreme CP time series, it was found that, with one exception (HRB), the average extreme CP after the mutation in the watershed areas was larger than that before the mutation, and eight watersheds showed an upward trend in extreme CP. This suggests that most watershed areas in China are at risk of extreme CP increases. This study can provide an important reference for the analysis of extreme CP in China.

Analysis of compound floods from storm surge and extreme precipitation in China

To enhance flood protection and risk reduction in China's coastal cities, it is crucial to comprehend the variability and underlying causes of compound floods along the coasts arising from storm surges and intense precipitation. The variability of compound floods from storm surge and extreme precipitation from 1958 to 2014 was analyzed using daily maximum sea level data and daily cumulative precipitation from 16 tide gauges along the coast of China. More than 70 % of the storm surges that caused damage from 1989 to 2014 at 14 out of 16 tide gauges were compound floods. We found a strong and seasonally varying dependence between storm surges and extreme precipitation along the coast of China. There were generally positive trends for the annual number of compound events at tide gauges south of 35°N. Compound flood variability was related to large-scale climate modes. The Arctic Oscillation in March was significantly negatively correlated with the compound events at tide gauges north of 35°N, while the opposite was true at tide gauges south of 35°N. The compound events were significantly correlated with the Pacific Decadal Oscillation (PDO) in June at 10 of the 16 tide gauges, and negatively correlated with the Niño1 + 2 index in February at 7 of the 16 tide gauges. Precipitation extremes determine the changes in the occurrence of compound floods, and by modifying the East Asian summer monsoon and typhoons, the large climate modes are supposed to impact the occurrence of compound floods. Our findings can enhance our understanding of compound floods and provide an important reference for flood risk management, particularly for low-lying coastal cities in China.

Analyzing temperature and precipitation extremes in China using multiple gridded datasets: A comparative evaluation

Gridded meteorological datasets offer valuable data sources for monitoring and studying climate extremes. Based on the 2291 CMA meteorological observations, this paper evaluates and compares seven high-resolution gridded datasets and a global climate extremes indices datasets, HadEX3. Temporal and spatial consistency are assessed at the location of CMA observations. High-resolution gridded products and HadEX3 can effectively monitor the inter-annual changes in the overall mean of extreme temperature. The temporal consistency of precipitation indices is lower due to uneven spatial distribution of precipitation. Each high-resolution gridded dataset shows good consistency in depicting the spatial distribution patterns of extreme climate. However, at locations of the observations, different indices have larger or smaller biases. These are mainly due to the spatial scale of gridded datasets and calculation and operation order of extreme indices. The study emphasizes the importance of in-situ observations in improving data accuracy. At the same time, it is found that the generation method of the dataset has an impact on the quality, and satellite and model simulation data as background fields can compensate for the lack of spatial coverage of int-situ observations. In the study of extreme climate, appropriate datasets should be selected according to different research purposes.

Spatial risk occurrence of extreme precipitation in China under historical and future scenarios

Spatial risk occurrence of extreme precipitation in China under historical and future scenarios

Spatiotemporal Changes in Extreme Precipitation in China’s Pearl River Basin during 1951–2015

Spatiotemporal Changes in Extreme Precipitation in China’s Pearl River Basin during 1951–2015

Time distribution pattern and spatial heterogeneity of hourly scale event-based extreme precipitation in China

Extreme precipitation occurs naturally in the form of “events,” and the spatio-temporal distribution pattern of event-based extreme precipitation is of great significance to flood disaster prevention and water resources protection. This study proposed the concept of hourly scale event-based extreme precipitation (HEEP), referring to a precipitation event with at least an hourly precipitation extreme (exceeding the 99th percentile threshold). Then we identified the dominant time distribution pattern (TDP), analyzed the seasonal proportion and distribution, and investigated the spatial distribution of three basic features (total frequency, amount, and duration) of HEEP from 2008 to 2017 in China and its nine major river basins. Results showed that over 90% of the areas in China were dominated by short-duration HEEP (S-HEEP, less than eight hours). While the rest areas were led by long-duration HEEP (L-HEEP, more than eight hours), mainly concentrated on the middle and lower reaches of the Yangtze River and southeast coastal areas of China. Areas dominated by S-HEEP were more than L-HEEP in different seasons except in winter. Three basic features of HEEP showed similar spatial distribution patterns of high in southeastern China and low in northwestern China, indicating HEEP frequently occurred in southeastern China with relatively large amounts and long duration. The concept of HEEP can provide new ideas for regional and global extreme precipitation research.

Climate Informed Non-stationary Modeling of Extreme Precipitation in China

Climate Informed Non-stationary Modeling of Extreme Precipitation in China

Sensitivity of the simulation of extreme precipitation events in China to different cumulus parameterization schemes and the underlying mechanisms

The ability of climate models to capture extreme precipitation events is crucially important, but most of the existing models contain significant biases for the simulation of extreme precipitation. To understand the causes of these biases, we used five different cumulus parameterization schemes in the regional Climate–Weather Research and Forecasting (CWRF) model to investigate its performance and biases in the simulation of extreme precipitation events in China. In general, the ensemble cumulus parameterization (ECP) scheme was the most skillful in reproducing the spatial distribution of the 95th percentile daily precipitation (P95) and the other four schemes either overestimated (the Kain-Fritsch Eta and Tiedtke schemes) or underestimated (the Betts-Miller-Janjic and New Simplified Arakawa-Schubert schemes) P95. Compared with the observational data, ECP scheme significantly improved the simulation of extreme precipitation in China and had the highest correlation and the smallest root-mean-square error in most areas and seasons. To clarify the underlying physical processes of P95 simulation biases, we established a regression model of extreme precipitation based on ECP scheme. This showed that P95 in North China is mainly affected by moisture convergence, planetary boundary layer height and lifting condensation level (relative importance 18–32%). In Central China, the vertical upward motion of water vapor, sensible heat flux and planetary boundary layer height (relative importance 18–30%) are main factors associated with P95. In South China, the vertical upward motion and horizontal transport of water vapor are predominant (relative importance 26–37%). In addition, the net surface energy, surface and atmospheric radiation flux, total precipitable water, convective available potential energy and cloud water path also have a high correlation with P95 (the second most important factor; relative importance 14–31%). The influence of each factor on the simulation of P95 is different when using different cumulus parameterization schemes and the interaction among the different factors determines the ability of CWRF model to simulate extreme precipitation. These results provide important references for future model evaluations and improvements.

Projection of compound wind and precipitation extremes in China based on Phase 6 of the Coupled Model Intercomparison Project models

AbstractExtreme precipitation and extreme wind may lead to catastrophic impacts on ecosystems, infrastructure, human dwellings and settlements. The concurrence of the two extremes, compound wind and precipitation extremes (CWPEs), may cause even larger impacts than the univariate counterparts. Under global warming with increased weather and climate extremes, there is a necessity to explore projected changes of CWPEs in the future. Based on daily precipitation and surface wind speed from observations (CN05.1) and simulations of Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models, we assess changes of CWPEs in the mainland of China in the future. By defining CWPEs based on the 98th percentile threshold in summer seasons (June–July–August [JJA]) in historical periods (1995–2014), we first explore the model performance in simulating precipitation, wind speed and CWPEs in China during JJA in historical periods. We then compute the projected changes of CWPEs in the near‐term (2021–2040), mid‐term (2041–2060) and long‐term (2081–2100) periods under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios. The occurrence of CWPEs is projected to increase in three future periods, particularly in the south and east China. Though a slight increase of CWPEs is shown under SSP1‐2.6 and SSP2‐4.5, the projected changes of CWPEs under SSP5‐8.5 show a dramatic increase (around 46%) in the long‐term period. Our assessment of projected changes in CWPEs can provide useful information to cope with the impact of CWPEs in China under climate change.

Projection of precipitation extremes in China's mainland based on the statistical downscaled data from 27 GCMs in CMIP6

Precipitation extremes have large influences on hydrological, ecological and agricultural systems. This study aimed to investigate variations of precipitation extremes in China's mainland over 1961–2100 based on 27 newly released General Circulation Models (GCMs) in Coupled Model Intercomparison Project phase 6 (CMIP6) under four shared socioeconomic pathway scenarios (SSPs). 10 extreme precipitation indices (EPIs) were extracted for quantifying changes of precipitation extremes. A statistical downscaling method was applied to downscale original precipitation data from the GCMs. Taylor diagram and interannual variability skill methods were used to evaluate performance of the downscaled GCM data for simulating the EPIs. The results displayed that: (1) The downscaled 27 GCMs performed well for the wet EPIs. Based on the multi-model ensemble results, more severe and frequent precipitation extremes over 2015–2100 were projected. (2) The largest changes in extreme precipitation intensities over 2015–2100 would occur in central and southeast China. The maximum changes in extreme precipitation frequencies would occur in Tibetan Plateau and central and southern China. (3) Precipitation extremes in Xinjiang and Tibetan Plateau would be more sensitive to climate change than other sub-regions. The future EPI generally have longer than 10-year of primary periods. (4) The GCMs had larger contribution than the SSPs to the uncertainty in projecting the 10 EPIs. (5) CMIP6 models performed better than CMIP5 models in reproducing interannual variations of EPIs. This study supplies useful information to cope with future intensified precipitation extremes in China.

The global warming and IPO impacts on summer extreme precipitation in China

The global warming and IPO impacts on summer extreme precipitation in China

Attributing observed increase in extreme precipitation in China to human influence

This paper examines new evidence from observational and detection and attribution studies of changes in extreme precipitation in China since the early 1960s. We have also designed a series of sensitivity tests to explore the robustness of detection and attribution results to the differences in sample size, in extreme precipitation index, and in data processing procedure. Our analyses used the most recent update of observational records as well as simulations conducted with the climate models participated in the Coupled Model Intercomparison Project Phase 6. Based on the existing studies and our additional analyses, we found that human influence is detectable in extreme precipitation in China regardless of the period, extreme precipitation index, or data treatment considered, in both China as a whole and in northern and southern China separately. We also found, as is often encountered in detection and attribution studies, it is difficult to separate the contribution from anthropogenic forcing from that of natural external forcing, and it is also challenging to decompose the anthropogenic component into a greenhouse gas forcing component and a component that reflects other anthropogenic forcing agents (dominantly, aerosols).

Projection of Future Extreme Precipitation in China Based on the CMIP6 from a Machine Learning Perspective

In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends.

Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes

An accurate and high spatiotemporal resolution precipitation observation dataset is crucial to the studies of climate change and related impact assessments. Great efforts have been made to evaluate the performance of a variety of precipitation products. However, most studies mainly focused on the assessment of precipitation estimation or precipitation occurrence detection, and seldom evaluated the capabilities of monitoring extreme precipitation of the utilized datasets. We conduct a study to quantitatively evaluate eight high-resolution precipitation products, including APHRODITE, CHIRPS, CMFD, CMORPH, IMERG, MSWEP, PERSIANN, and TMPA, against dense rain gauges in terms of precipitation climatology and extremes over China from 2001 to 2015. Results indicate that CMFD is superior to the rest products in terms of estimating precipitation amounts and monitoring precipitation extremes in China. APHRODITE performs best among all products in detecting precipitation occurrences. Precipitation products also exhibit different levels of performance in four subregions with distinct regional features. For both precipitation climatology and extremes, CMFD has the best applicability in the humid region and the Tibetan Plateau, while APHRODITE and MSWEP outperform other products in the wet-to-arid transition and arid regions, respectively. This work could provide a valuable basis for bias correction and downscaling of global climate model outputs. It also has implications for the selection of precipitation products for local studies and the future development of precipitation products in China.

Influence of urbanization on hourly extreme precipitation over China

The impact of rapid urbanization on the spatiotemporal pattern of short-term extreme precipitation in China remains unclear at the subnational scale. In this study, we present a general framework that measures urbanization-induced variation in hourly extreme wet season precipitation (April–October) from 1985 to 2012, with reference to a dynamic urban–rural station classification based on annual changes in urban extent. We found that urbanization in south China (<29° N) brings more extreme precipitation to urban areas than to suburbs, and reduces extreme precipitation continually over urban areas in parts of the north and northeast. Over 60% of provincial capital cities show significant changes in extreme precipitation due to urbanization, including smaller size cities separated from large urban clusters. Urbanization enhances extreme precipitation mainly in the local main part of the rainy season, which refers to May in the south (e.g. urban–rural differences of 0.70 mm h−1 in Guangzhou) and July–September in the central and north (1.16 mm h−1 in August of Beijing). Urbanization also increases hourly extreme precipitation at peak times in diurnal cycles. The results indicate that urbanization has caused overall more and more heterogeneous spatial patterns over China and concentrated distributions during the rainy season and peak time. These patterns warrant attention when assessing the risk of increased waterlogging and flash flooding in urban areas.