Increasing Impacts of Summer Extreme Precipitation and Heatwaves in Eastern China

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However, the accurate link between extreme precipitation events (EPEs) and heatwaves (HWs), and changes in these extremes and associated socio-economic impacts in eastern China have not been fully resolved. This study examines historical and future changes of summer EPEs and HWs in eastern China based on observations, reanalysis, and model outputs from the Coupled Model Intercomparison Project Phase 6. The results show that EPEs and HWs in eastern China have increased in the past four decades and are projected to rise in the future. The Yangtze River Basin and its southern regions will be confronted with the compound disaster of HWs and EPEs in the future projections. High values of the annual mean total person-times (estimated as population density multiplied by event frequency) affected by EPEs and HWs are observed in the North China Plain, Yangtze River Delta, Sichuan Basin, and southeast coast. The total person-times affected by EPEs show a slightly decreasing trend under both scenarios. However, the total person-times affected by HWs under Shared Socioeconomic Pathway (SSP) 245 scenario maximize at around 4.0 billion, lower than the peak person-times (about 5.0 billion) under SSP585 scenario. We further investigate the linkage of such extreme events with sea surface temperature and western North Pacific subtropical high anomalies. The correlations between the mean-state and extreme precipitation and maximum temperature anomalies both shifted from negative in the historical period to positive under future projections. Extreme Precipitation Heatwaves Eastern China CMIP6 Climate Projection SSP Scenarios Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Global-scale warming has been unequivocal over the past century and the global mean surface temperature is projected to increase by 1.1 to 5.4 ℃ in 2100 depending on different scenarios of greenhouse gases emissions (IPCC, 2014). Within an anthropogenically warming climate, extreme weather and climate events have been shown to occur more frequently and intensely (Coumou and Rahmstorf 2012, Dosio et al. 2018). Many previous studies suggest an increasing trend in extreme temperature, flood, and tropical cyclones over the past several decades, profoundly impacting the global ecosystem and socio-economic sectors (Emanuel 2005, Hansen et al. 2012, Perkins et al. 2012, Westra et al. 2013, Fischer and Knutti 2015, Donat et al. 2016). Heatwaves (HWs) and extreme precipitation events (EPEs) are among the most threatening acute meteorological events, which have received considerable attention from policymakers, the scientific community, and the general public (Stott et al. 2004, Lau and Nath 2012, Campbell et al. 2018, Wei Li et al. 2018, Ning et al. 2022). Climate model simulation and projections have shown an increasing frequency of EPEs and HWs as the Earth’s climate becomes warmer (O'Gorman and Schneider 2009, Fischer et al. 2013, Bao et al. 2017, Z. Li et al. 2019, Raghavendra et al. 2019). Extreme precipitation is more likely to occur in warmer seasons, as the saturation water vapor pressure increases by roughly 7%/℃ based on the Clausius–Clapeyron relation (Boer 1993, Trenberth et al. 2003, Held and Soden 2006). Allan and Soden (2008) addressed the changes of extreme precipitation in a warmed climate and concluded that wet regions become wetter and dry regions become drier. To the best of our knowledge, however, little research has been focused on understanding the relationship between HWs and EPEs within the context of global warming. Will HWs and EPEs occur simultaneously and exert a double threat on social and economic development? Moreover, the physical mechanisms dominating the variations and changes of EPEs and HWs in China have not been fully resolved. Previous studies have highlighted the significant impacts of large-scale atmospheric circulation (i.e., high-pressure anticyclones) and sea surface temperature (SST) on HWs (Wu and Wang, 2015, Wang et al. 2017, Rodrigues et al. 2019, Zheng and Wang, 2019, Xu et al. 2020). Summer EPEs in eastern China is primarily driven by the East Asian summer monsoon and western Pacific subtropical high (Zhou and Yu 2005, Zhu et al. 2011). Tropical cyclones (TCs) have also been reported to significantly influence summer precipitation in both eastern and southern China (Wu et al. 2007, Y. Ma et al. 2017). Nevertheless, till today, the knowledge of physics behind the EPEs and HWs remains limited. For example, the dominated SST mode affects the EPEs and HWs at a given study area remains ambiguous. Therefore, it is of great scientific interest to investigate the different influential factors of EPEs and HWs in eastern China. China as the most populated country in the world has experienced an increase in the frequency of HWs and EPEs during the past several decades (Sun and Ao 2013, Ma et al. 2015, Luo and Lau 2017, S. Ma et al. 2017, Ning et al. 2022). In 2022, China issued the first national red alert for extreme drought and heatwaves, with the maximum 2-m air temperature above 40 °C (104 °F) over a period of 48 hours or more in eastern China. Eastern China, in particular, features several megacities and accounts for over 70% of the national population, thus, its role in the socioeconomic development of China is unshakable (Zhu et al. 2011, Zhang et al. 2015). However, the economy and society of Eastern China are remarkably vulnerable to summer HWs and EPEs (Sun et al. 2014, Chen et al. 2017, Zhang et al. 2017, Zhang and Zhou 2020). The recent frequent occurrence of extreme temperature and precipitation inevitably raises questions regarding the effects of anthropogenic climate change on the intensity and frequency of HWs and EPEs in Eastern China under different scenarios of greenhouse gases emission. Global climate models are primary tools for investigating possible future changes in climate extremes (Jiang et al. 2015, Y. Li et al. 2019, Wang et al. 2019, Zhang et al. 2022). The Coupled Model Intercomparison Project Phase 6 (CMIP6) incorporates the most complete scientific experiments and the greatest amount of simulation data of the past 20 years (Eyring et al. 2016, Simpkins 2017). However, the commonality and difference between summer HWs and EPEs in the current and future have not yet been fully addressed by CMIP6. In this study, we focus on examining the spatiotemporal variation characteristics and projections of HWs and EPEs in eastern China during extended summer (June–September) based on historical data and CMIP6 models. The potential impacts of HWs and EPEs are evaluated on the population in eastern China from the present to the future. Finally, we investigate the potential drives of summer HWs and EPEs and establish the relationship between such events. Scientific answers to these questions can provide a comprehensive picture of the changes in and impact of HWs and EPEs in eastern China, allowing for the development of future response strategies and early-warning systems for extreme climate hazards. 2. Materials and methods 2.2 Data sources 2.2.1 Observational and reanalysis data The observational and reanalysis data used in this study are summarized in Table S1. The daily 2-m maximum surface air temperature (Tasmax) and precipitation (Pr) were obtained from the China Daily Surface Temperature/Precipitation Dataset (V2.0) at China Meteorological Data Service Center (CMDC) based on 2,472 meteorological stations in China (Chen et al. 2017). The daily satellite SST data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) High Resolution Dataset Version 2.1 (Huang et al. 2021). The monthly 500-hPa and 850-hPa geopotential height (hereafter referred to as Z500 and Z850, respectively) were extracted from the fifth-generation atmospheric analysis (ERA5) at the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al. 2018). To evaluate the potential impacts of HWs (EPEs), we estimated the total person-times in eastern China computed as the population density multiplied by the corresponding frequency of HWs (EPEs). The historical population density data in China were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). This dataset includes historical population density every five years from 1990 to 2015 on 1-km grids (Xu 2017). The downscaled annual population data in China under different Shared Socioeconomic Pathways (SSPs) scenarios (1 km from 2010 to 2100) were applied following Y. Chen et al. (2020). 2.2.2 Future data preparation and model evaluation The daily Tasmax and Pr outputs obtained from the CMIP6 models are used to assess the projected HWs and EPEs during 2021–2100 (Eyring et al. 2016). We use the SSP scenarios with different radiative forcing conditions (i.e., representative concentration pathway, hereafter RCP) to quantify future projections up to 2100 based on CMIP6 models (O'Neill et al. 2016). To compare the simulation against the observations and to analyze both a medium and a high emission pathway, we use only the models that have outputs for the three simulations: historical, SSP245, and SSP585. SSP245 is considered a “middle of the road” pathway, which combines intermediate population growth, emission, and challenges, for both mitigation and adaptation, stabilizing at 4.5 W/m 2 (RCP4.5). On the other hand, SSP585 describes a “fossil-fueled development” pathway, incorporating rapid population growth and high emission, stabilizing at 8.5 W/m 2 (RCP8.5) and resulting in high challenges for mitigation (Costa and Rodrigues 2021). The simulations are bilinearly interpolated into a 0.5° × 0.5° common grid. All climate model data are derived from the first realization (r1i1p1f1) to equally estimate each model. The multi-model ensemble mean method with equal weight across models is used to reduce model uncertainty. To evaluate the agreement of the daily historical Tasmax and Pr from CMIP6 with observations during the historical period, we use the Taylor diagrams to present the goodness of fit to observations by their correlation, centered root-mean-square difference (RMSD), and standard deviation (Taylor 2001). Fig. S1 displays the relative skills of Tasmax for 28 climate models (Table S2) and Pr for 32 climate models (Table S3) for the extended summer (June–September). The performance in terms of Tasmax is typically superior to that of Pr when comparing climate models based on historical data (1979–2014) during the same observational period. To ensure the mutual exclusion of HWs and EPEs during the extended summer, we did not select individual models with sufficiently high prediction performances of Tasmax and Pr. Instead, based on the Tasmax variable, six climate models with high performance (RMSD and standard deviation below 0.5, correlation exceeding 0.5) were selected, namely, EC-Earth3-Veg, GFDL-ESM4, INM-CM4-8, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and MRI-ESM2-0. Finally, INM-CM4-8 was excluded after comparing the spatial patterns of extreme precipitation with the observation data set (Fig. S2 and S3). The remaining five models were then used to detect future variability of HWs and EPEs in eastern China. 2.3 Methods 2.3.1 Definition of heatwave Either absolute or relative thresholds can be used to define HWs. A fixed threshold of a daily maximum temperature of 35 °C is commonly used to define an HW event in China (Tan et al. 2010). To better quantify the differences in regional climatology, a relative threshold (i.e., defined by percentile) for HWs is adopted in this study. For each calendar day, an HW event is identified if Tasmax exceeded the 95th percentile threshold for at least three consecutive days based on the 1979–2019 climatology period (Hobday et al. 2016, Chen et al. 2017, Freychet et al. 2017). The climatological threshold is calculated from data within an 11-day window centered on each calendar day and then applied with a 31-day moving average. This seasonally varying threshold allows heatwave events to occur at any time of the year (Hobday et al. 2016). Following several previous studies (Wang et al. 2017, Yao and Wang, 2021), we apply four metrics to represent HW characteristics, including the number of heatwaves (HWN), the total number of heatwave days (HWT), average heatwave duration (HWDU), and heatwave mean intensity (HWI). The details of their definition are listed in Table S4. 2.3.2 Extreme precipitation event A threshold of 1 mm is typically used to distinguish between wet and dry days because extremely light precipitation (< 1 mm) may be occasionally recorded as no precipitation (Guo et al. 2018, Wu et al. 2018). Similarly, to represent the regional characteristics of precipitation changes, the relative threshold definition (the percentile-based method) is used to identify EPEs in this study (Huang et al. 2018, Wu et al. 2018). Following Gao et al. (2017) and Huang et al. (2018), we applied the 95th percentile to identify extremely unusual precipitation events in this study. Therefore, for each grid point in eastern China, an EPE was defined as the daily precipitation exceeding the 41-year average of the 95th percentile for the summers from 1979 to 2019. The method for calculating the 95th percentile precipitation is described in Bonsal et al. (2001). If the precipitation at each point exceeds this threshold, an EPE is considered to have occurred. Further, if the precipitation for 1 day exceeds the threshold of a certain day, then namely a 1-day EPE event. Besides, we utilize the Pearson’s correlation analysis and linear trend test to further analyze the observations and model data. The significance level was calculated using the Mann-Kendall test. 3. Results 3.1 Spatiotemporal characteristics of summer EPEs during 1979–2019 Large spatial variations exist in the mean frequency of EPEs and amount of extreme precipitation (AEP), but as shown in Fig. 1, different spatial patterns of EPEs and AEP are identified. The regions with the largest EPEs are apparent in the south of the Yangtze River Basin and its western part, with an average of 3–5 EPEs yr -1 in the past 41 summers (Fig. 1a). Meanwhile, the regions with large mean AEP appear in the southeast coast, Yangtze River Basin, North China Plain, and the southern coast of Northeast Plain, ranging from 60 to 110 mm (Fig. 1b). Notably, we find a correspondence of spatial patterns between mean EPEs and precipitation, and the consistent structure between mean AEP and EPE thresholds (Fig. S4). High annual mean summer precipitation values (over 200 mm) are apparent in the South of the Yangtze River Basin, the southeast coast, and the central and western parts of the Yangtze River Basin, exhibiting a decreasing trend from southeast to northwest (Fig. S4a). The spatial distribution of the EPE threshold is similar to that of AEP, with high values reaching 30–70 mm (Fig. S4b). Furthermore, the spatial pattern of ratios between AEP and total precipitation shows consistency with that of the EPE threshold, with high values (over 25%) apparent in the North China Plain, eastern of the Yangtze River Basin, and southeast coast (Fig. S4c). In general, regions with more summer rainfall are prone to have more EPEs, while regions with high values of AEP are apparent in the eastern coastal areas and the Yangtze River Basin, and the percentage of AEP in total precipitation in these regions is also higher than other regions. Moreover, the spatial pattern of the 1-day EPEs (total number) suggest consistency with mean frequency of EPEs, with high values located in the south of the Yangtze River Basin and its western part, ranging from 120 to 190 counts (Fig. 2c). Meanwhile, the trend in total 1-day EPEs is increased by 149.9±87.1 counts per decade over 1979–2019, indicating the impact of warming climate on regional precipitation. As shown in Fig. 2d, there is a remarkable interannual variation in time series of total 1-day EPEs. 3.2 Spatiotemporal characteristics of summer HWs during 1979–2019 The high-value HWT areas are detected over the Yangtze River Basin and its northern regions, especially in the Yangtze River Delta and Sichuan Basin, where the corresponding HW duration ranges from 8 to 13 days (Fig. 3a). The high-value HWDU areas are relatively scattered, but mainly appear around the Yangtze River Basin, and the corresponding HW duration is 5–7 days/count (Fig. 3b). The HWN spatial pattern is similar to that of HWT, but the high values also appear in the Northeast Plain (Fig. 3c). The HWI spatial pattern shows an increasing trend from south to north, reaching a maximum of 7–10 ℃/count in northeastern China (Fig. 3d). Notably, high-value HWT and HWDU regions are both found in the Yangtze River Dealt, where is one of the most developed economies and densest population areas in China. Additionally, increasing trends are observed in the regional averaged HW metrics (Fig. 3). Both HWT and HWN metrics exhibit rapid increases over 1979–2019, reaching 0.69±0.22 days per decade and 0.12±0.03 counts per decade, respectively (Fig. 3e, g, p < 0.01). The trends in HWDU and HWI are 0.19±0.08 days/count per decade and 0.18±0.08 ℃/count per decade over 1979–2019, respectively (Fig. 3f, h, p < 0.1). We note that the spatial patterns and temporal variations of both HWT and HWN are very similar. Since HWT is equal to HWN multiplied by HWDU, HWT is mainly affected by HWN (Fig. 3). Furthermore, summer HWs in eastern China has strong interannual variations and severe HWs that usually occur during major El Niño years (e.g., 1982/83, 1997/98, 2009/10). 3.3 HWs and EPEs in CMIP6 projection Based on CMIP6 model projection, we evaluate the spatiotemporal distribution of HWs and EPEs by 2100 under two SSP/RCP-based scenarios. The result shows high HWT and EPE frequencies in the Yangtze River Basin and southern coastal regions. Under SSP245 scenario, HWT and frequency of EPEs reached 20–30 days and 3–6 counts, respectively, under SSP585 scenario, they reached 50–70 days and 5–6 counts, respectively (Fig. 4a, b). The southeastern coast shows relatively larger values of HWT and AEP than other regions in eastern China. HWT (AEP) reached 20–30 days (40–45 mm) under SSP245 scenario and 50–70 days (40–50 mm) under SSP585 scenario (Fig. 4c, d), indicating that a higher rate of population growth and radiative forcing conditions triggers more severe impacts on temperature and precipitation extremes. In general, the spatial patterns of HWN are consistent with those of HWT under SSP245 and SSP585 scenarios (Fig. 4). The regions with high HWN and EPE frequencies are identified in the Yangtze River Basin and southern coastal regions under SSP245 scenario, reaching 3.5–4.5 counts and 4–6 counts, respectively (Fig. 4e). Under SSP585 scenario, the regions with high value of HWN expanded, but the frequency of EPEs shows negligible changes (Fig. 4f). In the southeast coast of China, both HWN and AEP show relatively high values, reaching 3.5–4.5 counts and 40–45 mm under SSP245 scenario and 3.5–5.0 counts and 40–50 mm under SSP585 scenario (Fig. 4g, h). Yet, the differences between SSP245 and SSP585 scenarios are trivial in terms of HWDU and HWI (Fig. S5). The enhanced number of future HWs is accompanied with a rapid increase of HWT, and the high-value areas are extended to the northwestern and northeastern parts of the study area from SSP245 to SSP585 scenarios. Specifically, the high values of multi-year mean HWT range from 8 to 13 days over 1979–2019 (Fig. 3), which is projected to increase to 20–30 days and 50–70 days under SSP245 and SSP585 scenarios (Fig. 4). This indicates that the HWT increases from the semi-moon scale in the historical period to the near-month scale under SSP245 scenario and the bimonthly scale under SSP585 scenario. It is interesting to find that compared to the changes in the HW characteristics, there are limited changes in the frequency of EPEs and AEP from SSP245 to SSP585 scenarios. It is worth noting that summer HWs and EPEs pose a dual threat to the Yangtze River Basin and its southern region, which has the highest risk of natural disasters in the future. 3.4 Potential socioeconomic impact estimates The person-times describe the potential socioeconomic impacts from extreme event exposure with respect to population. Fig. 5 shows the spatial distributions of the annual mean person-times affected by EPEs and HWs. Consistent spatial patterns are detected for EPEs and HWs in eastern China, with high person-times in high-density population areas, such as the North China Plain, Yangtze River Delta, Sichuan Basin, and southeast coast. Time series of the total person-times affected by EPEs and HWs exhibit a significantly rising trends, showing a growth rate of 11.3 million per decade for EPEs and 21.6 million per decade for HWs ( p < 0.01). The person-times affected by EPEs is generally higher than that affected by HWs in eastern China, while the increase rate of person-times of HWs is nearly 2-fold than EPEs. Figure S6 shows the future changes in total person-times affected by EPEs and HWs in Eastern China under SSP245 and SSP585 scenarios from 2021 to 2100. The total person-times affected by EPEs range between 3.5 and 4.5 billion in eastern China, showing a slightly decreasing trend under both scenarios (Figs. S6a and S6c). The total person-times affected by HWs under SSP585 scenario show a linear increasing trend, peak at around 2065, and then decline by the end of 21 st century (Fig. S6d). Compared with the SSP585 scenario, we detect a lagged peak year at around 2080 for person-times with the SSP245 scenarios (Fig. S6b). We note that the total person-times affected by HWs under SSP245 scenario maximize at around 4.0 billion, lower than the peak person-times (about 5.0 billion) under SSP585 scenario. Considering the total population projection curves will diverge after 2040 between two future projections (green and blue lines in Fig. S6e), the potential socioeconomic impacts (as suggested by total person-times) in eastern China from EPEs and HWs under the SSP585 scenario is more severe than that under the SSP245 scenario. 4. Discussion 4.1 Increased HWs and EPEs in a warming climate Our results in HWs are consistent with several previous studies that eastern China has experienced frequent HWs in recent decades, and such HWs are expected to increase in frequency, severity, and duration (Guo et al. 2017, Wang et al. 2017, Dosio et al. 2018, Z. Li et al. 2019, Ning et al. 2022). The number of EPEs significantly increases in eastern China and is projected to continue to rise by 2100 (Wang and Zhou, 2005, Feng et al. 2011, Liu et al. 2015, Zhang et al. 2017, Dong et al. 2020). Specifically, we find that the frequency of EPEs shows upward trends with 0.09±0.01 counts per decade and 0.21±0.01 counts per decade under SSP245 and SSP585 scenarios, the trends of HWN also increased from 0.31±0.01 counts per decade under SSP245 to 0.50±0.02 counts per decade under SSP585 ( p < 0.01, Fig. 6a, c). The increase in the atmospheric water-holding capacity associated with a temperature increase (the Clausius–Clapeyron relation) considerably influences the changes in extreme precipitation intensity at a rate of ~7%/℃ under warmer climates (Pall et al. 2007, Allan and Soden 2008, Utsumi et al. 2011). The increase in AEP and HWT reaches 0.14±0.03 mm per decade and 2.61±0.10 days per decade under the SSP245 scenario, under SSP585 scenario, the frequency of AEP and HWT rises at a rate of 0.37±0.03 mm per decade and 7.77±0.21 days per decade ( p < 0.01, Fig. 6b, d). Notably, the trends of HWT and AEP under the SSP585 are 3.0-fold and 2.6-fold higher than those of SSP245, respectively. While the trends of HWN and EPE under the SSP585 are 1.6-fold and 2.3-fold higher than those of SSP245, respectively. This suggests that the intensity of HWs and EPEs changes faster than the frequency. The HWs are becoming more frequent, longer lasting, and more intense in eastern China under global warming background (Fig. S7). On the other hand, global warming will alter atmospheric circulation and evaporation in some regions, providing a richer source of water for precipitation (IPCC 2018). The synergy of these two factors increases the probability of EPEs in humid regions (Allan and Soden 2008) (Fig. S8). Summer Tasmax anomalies in eastern China are shown to increase by to 1 ℃ in 2020 and are projected to be 2.5 ℃ and 4.8 ℃ warmer under SSP245 and SSP585 scenarios than the period of 1850-1900 (Fig. S9). Consequently, the Yangtze River Basin and its southern regions will face the combined disasters of HWs and EPEs in summer in the future projections, becoming a regional climate change hotspot. 4.2 Underlying mechanisms and connections between HWs and EPEs Anthropogenic factors contributes to the rising occurrence of heavy precipitation and high-temperature extremes globally (Fischer and Knutti 2015, Dosio et al. 2018). The rapid increase in the risk of summer HWs and EPEs in easter China can also be partially attributed to global warming (Sun et al. 2014, Liu et al. 2015, Freychet et al. 2017). Furthermore, SST anomaly (SSTA) plays an important role in modulating HWs and EPEs in China (Zhu et al. 2011, Wang et al. 2017, Wei et al. 2020). When the SSTAs in the Indian Ocean and Tropical North Atlantic are positive, they contribute to the positive rainfall anomalies in South China and the southeast coast (Fig. 7c, d). The long-term climate variations in China in summer may be related to the warming trend of SST in the Indian Ocean (Hu et al. 2003) and teleconnections from the North Atlantic (Shang et al. 2020). Previous studies have shown that summer precipitation over central eastern and southern China are attributed to the warming trend of the ENSO-like SSTAs in the tropical Pacific, Indian Ocean, and North Atlantic, which will trigger anomalous anticyclonic circulation over Philippine Sea (Yang and Lau, 2004, Weijing Li et al. 2018, J. Liu et al. 2019). When the SSTAs in the west Pacific, Tropical North Atlantic, and the Indian Ocean are positive, they can cause widespread warming in eastern China (Fig. 7e, g, h). The summer three-ocean SSTAs have a greater influence on Tasmax than on precipitation in eastern China, but the influences have significant variations from region to region. For example, the influence of Indian Ocean SST on precipitation and maximum temperature in eastern China is opposite (Fig. 7c, g). Notably, the west Pacific and Niño 3.4 SST anomalies alone have little influence on the summer precipitation in eastern China (Fig. 7a, b, f), which confirms that tropical Indian Ocean SST warming acts like a capacitor affecting summer climate anomalies over the Indo-western Pacific and East Asia, and the three-ocean interactions through ocean-atmosphere coupling can modulate climate variability (Xie et al. 2009, Cai et al. 2019, Wang 2019). Moreover, summer EPEs and HWs in eastern China are closely related to the strength and location of the western North Pacific subtropical high (WNPSH), and their influences vary across space (Zhu et al. 2011, Freychet et al. 2017, Zhang et al. 2017). Summer Tasmax and frequency of HWs have a higher correlation with WNPSH than precipitation and frequency of EPEs (Fig. S10). The high value area of the correlation coefficient between anomalous Tasmax and 500-hPa geopotential height (GPH) appeared in the northern and eastern coasts of China, reaching above 0.6 ( p < 0.05), the correlation coefficient between the anomalous frequency of HWs and GPH was mainly range from 0.4 to 0.6 ( p < 0.05), and the spatial distribution of high value areas is scattered (Fig. S10 c, d). Notably, the intensification of the WNPSH is favorable for more summer HWs in eastern China under present climate, and more monsoon rainfall and HWs in future projections (Q. Liu et al. 2019, X. Chen et al. 2020, Li et al. 2021). In addition to WNPSH, other factors such as the tropical cyclones, Pacific Decadal Oscillation/Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation may contribute to the interdecadal or multidecadal variations in the EPEs and HWs in eastern China (Ding et al. 2009, Zhang et al. 2017). Detailed analysis of the impacts of these longer timescale factors on EPEs and HWs is beyond the scope of this manuscript. Intuitively, we expect cooler summers when it rains, while heatwaves often accompany droughts (Trenberth and Shea, 2005). In the historical period, the correlation between mean anomalous precipitation and Tasmax is negative in eastern China, while the regions with the positive correlation area appear in the eastern coast of China under SSP245 scenario and expand rapidly under SSP585 scenario (Fig. 8a, c, e). Climate models suggest that EPEs and HWs will become more common in an anthropogenically warmed climate (IPCC, 2014). The correlations between the frequencies of EPEs and HWs shifted from negative in the historical period to positive under future projections (Fig. 8b, d, f). These results reveal a distinct link between future rainfall and temperature, with increased EPEs significantly associated with HWs during summer (Raghavendra et al. 2019). In addition, summer precipitation and maximum temperature were negatively correlated, consistent with the argument that warmer summers tend to be dryer, but this negative correlation is largely reduced in extreme cases. HWs are strongly linked to global warming, and previous studies have shown a significant increase in global HW activity from present climate to future projections (Hu et al. 2003, Freychet et al. 2017, Dosio et al. 2018, Perkins-Kirkpatrick and Lewis 2020). However, the compound disasters of HWs and EPEs in a warming climate are rarely studied. In eastern China, as the correlations between the frequencies of EPEs and HWs shifts from negative to positive in summer, this will lead to future loss of life and property and enormous socioeconomic consequences. It further supports the urgent need for policymakers to take action to curb greenhouse emissions. 5. Conclusion EPEs and HWs can significantly affect the socioeconomic losses and human health risks related to extreme climate events during summer, especially in the densely populated and economically active areas of the East Asian summer monsoon region. This study attempted to investigate the current and future spatiotemporal characteristics of EPEs and HWs in eastern China and the impacts of HWs on EPEs changes in a warming world. The main conclusions based on the above analysis are as follows: 1. The regions with the largest EPEs are apparent in the south of the Yangtze River Basin and its western part, with an average of 3–5 EPEs yr -1 during 1979–2019, the regions with large mean AEP appear in the southeast coast, Yangtze River Basin, North China Plain, and the southern coast of Northeast Plain, ranging from 60 to 110 mm. The metrics of HWs all show increase trends over 1979–2019, and the increased HWN resulted in a rapid increase of HWT, with rates of 0.69±0.22 days per decade and 0.12±0.03 counts per decade, respectively. 2. In terms of spatial variation, the high values of multi-year mean HWT are 20–30 days and 50–70 days under SSP245 and SSP585 scenarios, respectively. The increased HWDU and HWN lead to a rapid increase in HWT. However, there is little change in the frequency of EPEs and AEP from SSP245 to SSP585 scenarios. In terms of temporal variation, HWs and EPEs are both increased in future projections. The Yangtze River Basin and its southern regions will face the compound disaster of HWs and EPEs in summer in the future projections. 3. High values of the annual mean total person-times affected by EPEs and HWs are observed in the North China Plain, Yangtze River Delta, Sichuan Basin, and southeast coast, with over four million person-times. The total person-times affected by EPEs range between 3.5 billion to 4.5 billion in eastern China shows a slightly decrease trend under both scenarios. The total person-times affected by HWs under SSP245 scenario maximize at around 4.0 billion, lower than the peak person-times (about 5.0 billion) under SSP585 scenario. 4. The increased EPEs and HWs are primarily caused by anthropogenic warming. Positive summer SSTs (West Pacific, Tropical North Atlantic, and the Indian Ocean) can cause widespread warming in eastern China and a regional increase in rainfall in the Yangtze River Basin. Moreover, summer Tasmax and frequency of HWs have a higher correlation with WNPSH than precipitation and frequency of EPEs. We detect strong positive correlation between mean-state (Tasmax or Precipitation) and extreme climate (HWs or EPEs), this implies that improved prediction of mean-state climate can provide insights into the extreme prediction. In eastern China, summer precipitation and temperature were negatively correlated, consistent with the argument that warmer summers tend to be dryer, but this negative correlation is largely reduced in extreme cases. As the correlations between the frequencies of EPEs and HWs shifts from negative to positive in summer, this will lead to future loss of life and property and enormous socioeconomic consequences. Declarations Data Availability All the datasets used in this study are publicly available. The NOAA High Resolution SST V2.1 data is provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. ERA5 dataset is available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The daily 2-m maximum surface air temperature and precipitation datasets are available at http://data.cma.cn/en. 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Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2023 Read the published version in Climatic Change → Version 1 posted Reviewers invited by journal 24 Oct, 2022 Reviewers agreed at journal 05 Oct, 2022 Editor assigned by journal 30 Sep, 2022 First submitted to journal 28 Sep, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2114246","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":142081788,"identity":"8cf0fc4e-71e6-41c5-9177-5943fd2c2927","order_by":0,"name":"Yulong Yao","email":"","orcid":"","institution":"State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"Yao","suffix":""},{"id":142081789,"identity":"945c1b51-b9e5-4f1c-babf-fba9d28ac7b6","order_by":1,"name":"Wei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYFACxsYHHxgYeBACBwhqYW42nAHRwthApBb2NmmoFURqMTh/sE3adsc9GXP2w8cffNzBIMd3I4GAlgMHm61zzxTzWPakJTbOPMNgLElIi9nBxsbbuW0JPAY3eAybedsYEjcQ1HKYsUHaEqyF/yNISz1hLccYm6QZIbYwgrQkGBDSYn+Gsdmw9wxQy5k0w5kz2yQMZ555gF+LZP/xhw9+7kiwNzh++MGHj2028nzHCdgCBtAIAQEJIpSjaRkFo2AUjIJRgAkAX0VI93FbiGoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7179-5697","institution":"Princeton University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":142081790,"identity":"b49a2a36-ed3f-4c14-bd62-014f7f533b0e","order_by":2,"name":"Ben Kirtman","email":"","orcid":"","institution":"University of Miami Rosenstiel School of Marine and Atmospheric Science","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Kirtman","suffix":""}],"badges":[],"createdAt":"2022-09-28 22:49:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2114246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2114246/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10584-023-03610-4","type":"published","date":"2023-09-19T15:00:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":27578253,"identity":"2c8650de-d29e-4835-8943-be88b4ca54b7","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2111651,"visible":true,"origin":"","legend":"\u003cp\u003eTopography and the Kuroshio path in the study area. Eastern China is in the east of the cyan line (100 °E).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/7069cc15d494a922ad9b627a.png"},{"id":27578997,"identity":"e0d4c586-5d1c-4cb5-a324-b7d79f2d5d3c","added_by":"auto","created_at":"2022-10-10 19:29:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1178322,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of the multi-year average (a) frequency of EPEs and (b) AEP in summer over 1979–2019. Spatial distribution of total (c) 1-day EPEs and (d) its corresponding variation in summer over 1979–2019. Purple lines represent the Yangtze River Basin and the dashed line represents the linear trend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/afc5abb97db7f6353aefee2e.png"},{"id":27578248,"identity":"7e4b3155-3892-410a-8514-8c9f2c01e859","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1127289,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of the multi-year average of summer heatwave metrics (left column) and their corresponding temporal variations (right column) in eastern China from 1982 to 2019. (a, e) mean HWT, (b, f) mean HWDU, (c, g) mean HWN, and (d, h) mean HWI. The purple lines delimit the Yangtze River Basin and the dashed lines represent the linear trend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/b837cbb2a9773878aab76821.png"},{"id":27578254,"identity":"5f60c1ab-7294-487d-a969-51ed93003275","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1315721,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of\u003cstrong\u003e \u003c/strong\u003emulti-year averaged (left column) HWT (shading area, Unit: Days) with the frequency of EPEs (blue contour line, Unit: Counts) and AEP (green contour line, Unit: mm), (right column) HWN (shaded area, Unit: Counts) with the frequency of EPEs and AEP under SSP245 (a, c, e, and g) and SSP585 (b, d, f, and h) scenarios in eastern China over 2021–2100. The purple lines present the Yangtze River Basin.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/a5e10246c73836b727d3c9e3.png"},{"id":27578252,"identity":"33814563-8254-482c-88a4-02be21357a12","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":815225,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of multi-year average person-times affected by frequencies of (a) EPEs and (b) HWs in eastern China over 1979–2019. The inset charts represent the annual average of person-times affected by EPEs (green line) and HWs (pink line). Dashed lines represent the linear trend and the purple lines present the Yangtze River Basin.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/8cbd454f78bfb2e83dcc1c1d.png"},{"id":27578249,"identity":"593ee185-0917-4d34-aabc-829ac617175a","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":826409,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variations of the regionally averaged summer frequency of EPEs and AEP compared with HWN under SSP245 and SSP585 scenarios in eastern China over 2021–2100. (a) Frequency of EPEs and HWN under SSP245, (b) AEP and HWT under SSP245, (c) EPEs and HWN under SSP585, and (d) AEP and HWT under SSP585. Dashed blue and red lines represent the linear trend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/b1a8bbf9e1eceb0934878ce8.png"},{"id":27578250,"identity":"3c6987cb-e4ef-4538-b5a4-62aadc85511f","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1406714,"visible":true,"origin":"","legend":"\u003cp\u003eAnomalous summer rainfall (left column, unit: mm) and Tasmax fields (right column, unit: ℃) regressed onto the (a, e) West Pacific SSTA (0–10°N, 130°E–150°E), (b, f) Niño 3.4 SSTA (5°S–5°N, 170°W–120°W), (c, g) Indian Ocean SSTA (10°S–20°N, 50°E–90°E), and (d, h) Tropical North Atlantic SSTA (0°–25°N, 15°W–90°W). The areas significant at the 95% confidence level are dotted. The purple lines delimit the Yangtze River Basin.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/f302c2a0c4840a7a9b42e756.png"},{"id":27578256,"identity":"996275d1-79e3-4542-9ff6-1d25aee77f1f","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1078099,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation maps between summer (a, c, and e) precipitation and Tasmax anomalies, (b, d, and f) frequencies of EPEs and HWs anomalies from the historical period (1979–2019) to future projections (2021–2100). The areas significant at the 95% confidence level are dotted. The purple lines delimit the Yangtze River Basin.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/a5aa4cfa13f361d0b7b4696d.png"},{"id":43640281,"identity":"31cfd93e-232d-4286-81b0-fdf30cca3f80","added_by":"auto","created_at":"2023-09-25 15:02:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9574980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/93298790-196b-4f25-8144-becd0cc085da.pdf"},{"id":27578255,"identity":"b0ea6b0b-1bc2-47e4-b612-93ff59d103ea","added_by":"auto","created_at":"2022-10-10 19:24:57","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":4223455,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2114246/v1/dd641199d5c2d08a7a1d5d79.docx"}],"financialInterests":"","formattedTitle":"Increasing Impacts of Summer Extreme Precipitation and Heatwaves in Eastern China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal-scale warming has been unequivocal over the past century and the global mean surface temperature is projected to increase by 1.1 to 5.4 ℃ in 2100 depending on different scenarios of greenhouse gases emissions (IPCC, 2014). Within an anthropogenically warming climate, extreme weather and climate events have been shown to occur more frequently and intensely (Coumou and Rahmstorf 2012, Dosio et al. 2018). Many previous studies suggest an increasing trend in extreme temperature, flood, and tropical cyclones over the past several decades, profoundly impacting the global ecosystem and socio-economic sectors (Emanuel 2005, Hansen et al. 2012, Perkins et al. 2012, Westra et al. 2013, Fischer and Knutti 2015, Donat et al. 2016).\u003c/p\u003e\n\u003cp\u003eHeatwaves (HWs) and extreme precipitation events (EPEs) are among the most threatening acute meteorological events, which have received considerable attention from policymakers, the scientific community, and the general public (Stott et al. 2004, Lau and Nath 2012, Campbell et al. 2018, Wei Li et al. 2018, Ning et al. 2022). Climate model simulation and projections have shown an increasing frequency of EPEs and HWs as the Earth\u0026rsquo;s climate becomes warmer (O\u0026apos;Gorman and Schneider 2009, Fischer et al. 2013, Bao et al. 2017, Z. Li et al. 2019, Raghavendra et al. 2019). Extreme precipitation is more likely to occur in warmer seasons, as the saturation water vapor pressure increases by roughly 7%/℃ based on the Clausius\u0026ndash;Clapeyron relation (Boer 1993, Trenberth et al. 2003,\u0026nbsp;Held and Soden 2006). Allan and Soden (2008) addressed the changes of extreme precipitation in a warmed climate and concluded that wet regions become wetter and dry regions become drier. To the best of our knowledge, however, little research has been focused on understanding the relationship between HWs and EPEs within the context of global warming. Will HWs and EPEs occur simultaneously and exert a double threat on social and economic development?\u003c/p\u003e\n\u003cp\u003eMoreover, the physical mechanisms dominating the variations and changes of EPEs and HWs in China have not been fully resolved. Previous studies have highlighted the significant impacts of large-scale atmospheric circulation (i.e., high-pressure anticyclones) and sea surface temperature (SST) on HWs (Wu and Wang, 2015, Wang et al. 2017, Rodrigues et al. 2019, Zheng and Wang, 2019, Xu et al. 2020). Summer EPEs in eastern China is primarily driven by the East Asian summer monsoon and western Pacific subtropical high (Zhou and Yu 2005, Zhu et al. 2011). Tropical cyclones (TCs) have also been reported to significantly influence summer precipitation in both eastern and southern China (Wu et al. 2007, Y. Ma et al. 2017). Nevertheless, till today, the knowledge of physics behind the EPEs and HWs remains limited. For example, the dominated SST mode affects the EPEs and HWs at a given study area remains ambiguous. Therefore, it is of great scientific interest to investigate the different influential factors of EPEs and HWs in eastern China.\u003c/p\u003e\n\u003cp\u003eChina as the most populated country in the world has experienced an increase in the frequency of HWs and EPEs during the past several decades (Sun and Ao 2013, Ma et al. 2015, Luo and Lau 2017, S. Ma et al. 2017, Ning et al. 2022). In 2022, China issued the first national red alert for extreme drought and heatwaves, with the maximum 2-m air temperature above 40 \u0026deg;C (104 \u0026deg;F) over a period of 48 hours or more in eastern China. Eastern China, in particular, features several megacities and accounts for over 70% of the national population, thus, its role in the socioeconomic development of China is unshakable (Zhu et al. 2011, Zhang et al. 2015). However, the economy and society of Eastern China are remarkably vulnerable to summer HWs and EPEs (Sun et al. 2014, Chen et al. 2017, Zhang et al. 2017, Zhang and Zhou 2020). The recent frequent occurrence of extreme temperature and precipitation inevitably raises questions regarding the effects of anthropogenic climate change on the intensity and frequency of HWs and EPEs in Eastern China under different scenarios of greenhouse gases emission. Global climate models are primary tools for investigating possible future changes in climate extremes (Jiang et al. 2015, Y. Li et al. 2019, Wang et al. 2019, Zhang et al. 2022). The Coupled Model Intercomparison Project Phase 6 (CMIP6) incorporates the most complete scientific experiments and the greatest amount of simulation data of the past 20 years (Eyring et al. 2016, Simpkins 2017). However, the commonality and difference between summer HWs and EPEs in the current and future have not yet been fully addressed by CMIP6.\u003c/p\u003e\n\u003cp\u003eIn this study, we focus on examining the spatiotemporal variation characteristics and projections of HWs and EPEs in eastern China during extended summer (June\u0026ndash;September) based on historical data and CMIP6 models. The potential impacts of HWs and EPEs are evaluated on the population in eastern China from the present to the future. Finally, we investigate the potential drives of summer HWs and EPEs and establish the relationship between such events. Scientific answers to these questions can provide a comprehensive picture of the changes in and impact of HWs and EPEs in eastern China, allowing for the development of future response strategies and early-warning systems for extreme climate hazards.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.2 Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Observational and reanalysis data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe observational and reanalysis data used in this study\u0026nbsp;are summarized in\u0026nbsp;Table S1. The daily 2-m maximum surface air temperature (Tasmax) and precipitation (Pr) were obtained from the China Daily Surface Temperature/Precipitation Dataset (V2.0)\u0026nbsp;at China Meteorological Data Service Center (CMDC) based on 2,472 meteorological stations in China (Chen et al. 2017). The daily satellite SST data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) High Resolution Dataset Version 2.1 (Huang et al. 2021). The monthly 500-hPa and 850-hPa geopotential height (hereafter referred to as Z500 and Z850, respectively) were extracted from the fifth-generation atmospheric analysis (ERA5) at the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al. 2018).\u003c/p\u003e\n\u003cp\u003eTo evaluate the potential impacts of HWs (EPEs), we estimated the total person-times in eastern China computed as the population density multiplied by the corresponding frequency of HWs (EPEs). The historical population density data in China were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). This dataset includes historical population density every five years from 1990 to 2015 on 1-km grids (Xu 2017). The downscaled annual population data in China under different Shared Socioeconomic Pathways (SSPs) scenarios (1 km from 2010 to 2100) were applied following Y. Chen et al. (2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Future data preparation and model evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe daily Tasmax and Pr outputs obtained from the CMIP6 models are used to assess the projected HWs and EPEs during 2021\u0026ndash;2100 (Eyring et al. 2016). We use the SSP scenarios with different radiative forcing conditions (i.e., representative concentration pathway, hereafter RCP) to quantify future projections up to 2100 based on CMIP6 models (O\u0026apos;Neill et al. 2016). To compare the simulation against the observations and to analyze both a medium and a high emission pathway, we use only the models that have outputs for the three simulations: historical, SSP245, and SSP585. SSP245 is considered a \u0026ldquo;middle of the road\u0026rdquo; pathway, which combines intermediate population growth, emission, and challenges, for both mitigation and adaptation, stabilizing at 4.5 W/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(RCP4.5). On the other hand, SSP585 describes a \u0026ldquo;fossil-fueled development\u0026rdquo; pathway, incorporating rapid population growth and high emission, stabilizing at 8.5 W/m\u003csup\u003e2\u003c/sup\u003e (RCP8.5) and resulting in high challenges for mitigation (Costa and Rodrigues 2021). The simulations are bilinearly interpolated into a 0.5\u0026deg; \u0026times; 0.5\u0026deg; common grid. All climate model data are derived from the first realization (r1i1p1f1) to equally estimate each model. The multi-model ensemble mean method with equal weight across models is used to reduce model uncertainty.\u003c/p\u003e\n\u003cp\u003eTo evaluate the agreement of the daily historical Tasmax and Pr from CMIP6 with observations during the historical period, we use the Taylor diagrams to present the goodness of fit to observations by their correlation, centered root-mean-square difference (RMSD), and standard deviation (Taylor 2001). Fig. S1 displays the relative skills of Tasmax for 28 climate models (Table S2) and Pr for 32 climate models (Table S3) for the extended summer (June\u0026ndash;September). The performance in terms of Tasmax is typically superior to that of Pr when comparing climate models based on historical data (1979\u0026ndash;2014) during the same observational period. To ensure the mutual exclusion of HWs and EPEs during the extended summer, we did not select individual models with sufficiently high prediction performances of Tasmax and Pr. Instead, based on the Tasmax variable, six climate models with high performance (RMSD and standard deviation below 0.5, correlation exceeding 0.5) were selected, namely, EC-Earth3-Veg, GFDL-ESM4, INM-CM4-8, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and MRI-ESM2-0. Finally, INM-CM4-8 was excluded after comparing the spatial patterns of extreme precipitation with the observation data set (Fig. S2 and S3). The remaining five models were then used to detect future variability of HWs and EPEs in eastern China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u003c/strong\u003e \u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDefinition of heatwave\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEither absolute or relative thresholds can be used to define HWs. A fixed threshold of a daily maximum temperature of 35 \u0026deg;C is commonly used to define an HW event in China (Tan et al. 2010). To better quantify the differences in regional climatology, a relative threshold (i.e., defined by percentile) for HWs is adopted in this study. For each calendar day, an HW event is identified if Tasmax exceeded the 95th percentile threshold for at least three consecutive days based on the 1979\u0026ndash;2019 climatology period (Hobday et al. 2016, Chen et al. 2017, Freychet et al. 2017). The climatological threshold is calculated from data within an 11-day window centered on each calendar day and then applied with a 31-day moving average. This seasonally varying threshold allows heatwave events to occur at any time of the year (Hobday et al. 2016). Following several previous studies (Wang et al. 2017, Yao and Wang, 2021), we apply four metrics to represent HW characteristics, including the number of heatwaves (HWN), the total number of heatwave days (HWT), average heatwave duration (HWDU), and heatwave mean intensity (HWI). The details of their definition are listed in Table S4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eExtreme precipitation event\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA threshold of 1 mm is typically used to distinguish between wet and dry days because extremely light precipitation (\u0026lt; 1 mm) may be occasionally recorded as no precipitation (Guo et al. 2018, Wu et al. 2018). Similarly, to represent the regional characteristics of precipitation changes, the relative threshold definition (the percentile-based method) is used to identify EPEs in this study (Huang et al. 2018, Wu et al. 2018). Following Gao et al. (2017) and Huang et al. (2018), we applied the 95th percentile to identify extremely unusual precipitation events in this study. Therefore, for each grid point in eastern China, an EPE was defined as the daily precipitation exceeding the 41-year average of the 95th percentile for the summers from 1979 to 2019. The method for calculating the 95th percentile precipitation is described in Bonsal et al. (2001). If the precipitation at each point exceeds this threshold, an EPE is considered to have occurred. Further, if the precipitation for 1 day exceeds the threshold of a certain day, then namely a 1-day EPE event. Besides, we utilize the Pearson\u0026rsquo;s correlation analysis and linear trend test to further analyze the observations and model data. The significance level was calculated using the Mann-Kendall test.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Spatiotemporal characteristics of summer EPEs during 1979\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLarge spatial variations exist in the mean frequency of EPEs and amount of extreme precipitation (AEP), but as shown in\u0026nbsp;Fig. 1, different spatial patterns of EPEs and AEP are identified. The regions with the largest EPEs are apparent in the south of the Yangtze River Basin and its western part, with an average of 3\u0026ndash;5 EPEs yr\u003csup\u003e-1\u003c/sup\u003e in the past 41 summers (Fig. 1a). Meanwhile, the regions with large mean AEP appear in the southeast coast, Yangtze River Basin, North China Plain, and the southern coast of Northeast Plain, ranging from 60 to 110 mm (Fig. 1b). Notably, we find a correspondence of spatial patterns between mean EPEs and precipitation, and the consistent structure between mean AEP and EPE thresholds (Fig. S4). High annual mean summer precipitation values (over 200 mm) are apparent in the South of the Yangtze River Basin, the southeast coast, and the central and western parts of the Yangtze River Basin, exhibiting a decreasing trend from southeast to northwest (Fig. S4a). The spatial distribution of the EPE threshold is similar to that of AEP, with high values reaching 30\u0026ndash;70 mm (Fig. S4b). Furthermore, the spatial pattern of ratios between AEP and total precipitation shows consistency with that of the EPE threshold, with high values (over 25%) apparent in the North China Plain, eastern of the Yangtze River Basin, and southeast coast (Fig. S4c). In general, regions with more summer rainfall are prone to have more EPEs, while regions with high values of AEP are apparent in the eastern coastal areas and the Yangtze River Basin, and the percentage of AEP in total precipitation in these regions is also higher than other regions.\u003c/p\u003e\n\u003cp\u003eMoreover, the spatial pattern of the 1-day EPEs (total number) suggest consistency with mean frequency of EPEs, with high values located in the south of the Yangtze River Basin and its western part, ranging from 120 to 190 counts (Fig. 2c). Meanwhile, the trend in total 1-day EPEs is increased by 149.9\u0026plusmn;87.1 counts per decade over 1979\u0026ndash;2019, indicating the impact of warming climate on regional precipitation. As shown in Fig. 2d, there is a remarkable interannual variation in time series of total 1-day EPEs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Spatiotemporal characteristics of summer HWs during 1979\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe high-value HWT areas are detected over the Yangtze River Basin and its northern regions, especially in the Yangtze River Delta and Sichuan Basin, where the corresponding HW duration ranges from 8 to 13 days (Fig. 3a). The high-value HWDU areas are relatively scattered, but mainly appear around the Yangtze River Basin, and the corresponding HW duration is 5\u0026ndash;7 days/count (Fig. 3b). The HWN spatial pattern is similar to that of HWT, but the high values also appear in the Northeast Plain (Fig. 3c). The HWI spatial pattern shows an increasing trend from south to north, reaching a maximum of 7\u0026ndash;10 ℃/count in northeastern China (Fig. 3d). Notably, high-value HWT and HWDU regions are both found in the Yangtze River Dealt, where is one of the most developed economies and densest population areas in China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, increasing trends are observed in the regional averaged HW metrics (Fig. 3). Both HWT and HWN metrics exhibit rapid increases over 1979\u0026ndash;2019, reaching 0.69\u0026plusmn;0.22 days per decade and 0.12\u0026plusmn;0.03 counts per decade, respectively (Fig. 3e, g, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). The trends in HWDU and HWI are 0.19\u0026plusmn;0.08 days/count per decade and 0.18\u0026plusmn;0.08 ℃/count per decade over 1979\u0026ndash;2019, respectively (Fig. 3f, h, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1). We note that the spatial patterns and temporal variations of both HWT and HWN are very similar. Since HWT is equal to HWN multiplied by HWDU, HWT is mainly affected by HWN (Fig. 3). Furthermore, summer HWs in eastern China has strong interannual variations and severe HWs that usually occur during major El Ni\u0026ntilde;o years (e.g., 1982/83, 1997/98, 2009/10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 HWs and EPEs in CMIP6 projection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on CMIP6 model projection, we evaluate the spatiotemporal distribution of HWs and EPEs by 2100 under two SSP/RCP-based scenarios. The result shows high HWT and EPE frequencies in the Yangtze River Basin and southern coastal regions. Under SSP245 scenario, HWT and frequency of EPEs reached 20\u0026ndash;30 days and 3\u0026ndash;6 counts, respectively, under SSP585 scenario, they reached 50\u0026ndash;70 days and 5\u0026ndash;6 counts, respectively (Fig. 4a, b). The southeastern coast shows relatively larger values of HWT and AEP than other regions in eastern China. HWT (AEP) reached 20\u0026ndash;30 days (40\u0026ndash;45 mm) under SSP245 scenario and 50\u0026ndash;70 days (40\u0026ndash;50 mm) under SSP585 scenario (Fig. 4c, d), indicating that a higher rate of population growth and radiative forcing conditions triggers more severe impacts on temperature and precipitation extremes. In general, the spatial patterns of HWN are consistent with those of HWT under SSP245 and SSP585 scenarios (Fig. 4). The regions with high HWN and EPE frequencies are identified in the Yangtze River Basin and southern coastal regions under SSP245 scenario, reaching 3.5\u0026ndash;4.5 counts and 4\u0026ndash;6 counts, respectively (Fig. 4e). Under SSP585 scenario, the regions with high value of HWN expanded, but the frequency of EPEs shows negligible changes (Fig. 4f). In the southeast coast of China, both HWN and AEP show relatively high values, reaching 3.5\u0026ndash;4.5 counts and 40\u0026ndash;45 mm under SSP245 scenario and 3.5\u0026ndash;5.0 counts and 40\u0026ndash;50 mm under SSP585 scenario (Fig. 4g, h). Yet, the differences between SSP245 and SSP585 scenarios are trivial in terms of HWDU and HWI (Fig. S5).\u003c/p\u003e\n\u003cp\u003eThe enhanced number of future HWs is accompanied with a rapid increase of HWT, and the high-value areas are extended to the northwestern and northeastern parts of the study area from SSP245 to SSP585 scenarios. Specifically, the high values of multi-year mean HWT range from 8 to 13 days over 1979\u0026ndash;2019 (Fig. 3), which is projected to increase to 20\u0026ndash;30 days and 50\u0026ndash;70 days under SSP245 and SSP585 scenarios (Fig. 4). This indicates that the HWT increases from the semi-moon scale in the historical period to the near-month scale under SSP245 scenario and the bimonthly scale under SSP585 scenario. It is interesting to find that compared to the changes in the HW characteristics, there are limited changes in the frequency of EPEs and AEP from SSP245 to SSP585 scenarios. It is worth noting that summer HWs and EPEs pose a dual threat to the Yangtze River Basin and its southern region, which has the highest risk of natural disasters in the future.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Potential socioeconomic impact estimates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe person-times describe the potential socioeconomic impacts from extreme event exposure with respect to population. Fig. 5 shows the spatial distributions of the annual mean person-times affected by EPEs and HWs. Consistent spatial patterns are detected for EPEs and HWs in eastern China, with high person-times in high-density population areas, such as the North China Plain, Yangtze River Delta, Sichuan Basin, and southeast coast. Time series of the total person-times affected by EPEs and HWs exhibit a significantly rising trends, showing a growth rate of 11.3 million per decade for EPEs and 21.6 million per decade for HWs (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). The person-times affected by EPEs is generally higher than that affected by HWs in eastern China, while the increase rate of person-times of HWs is nearly 2-fold than EPEs.\u003c/p\u003e\n\u003cp\u003eFigure S6\u0026nbsp;shows the future changes in total person-times affected by EPEs and HWs in Eastern China under SSP245 and SSP585 scenarios from 2021 to 2100. The total person-times affected by EPEs range between 3.5\u0026nbsp;and\u0026nbsp;4.5 billion in eastern China, showing a slightly decreasing trend under both scenarios (Figs. S6a and S6c). The total person-times affected by HWs under\u0026nbsp;SSP585 scenario\u0026nbsp;show a linear increasing trend, peak at around 2065, and then decline by the end of 21\u003csup\u003est\u003c/sup\u003e century (Fig. S6d). Compared with the SSP585 scenario, we detect a lagged peak year at around 2080 for person-times with the SSP245 scenarios (Fig. S6b). We note that the total person-times affected by HWs under SSP245 scenario maximize at around 4.0 billion, lower than the peak person-times (about 5.0 billion) under SSP585 scenario. Considering the total population projection curves will diverge after 2040 between two future projections (green and blue lines in Fig. S6e), the potential socioeconomic impacts (as suggested by total person-times) in eastern China from EPEs and HWs under the SSP585 scenario is more severe than that under the SSP245 scenario.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Increased HWs and EPEs in a warming climate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results in HWs are consistent with several previous studies that eastern China has experienced frequent HWs in recent decades, and such HWs are expected to increase in frequency, severity, and duration (Guo et al. 2017, Wang et al. 2017, Dosio et al. 2018, Z. Li et al. 2019, Ning et al. 2022). The number of EPEs significantly increases in eastern China and is projected to continue to rise by 2100 (Wang and Zhou, 2005, Feng et al. 2011, Liu et al. 2015, Zhang et al. 2017, Dong et al. 2020). Specifically, we find that the frequency of EPEs shows upward trends with 0.09\u0026plusmn;0.01 counts per decade and 0.21\u0026plusmn;0.01 counts per decade under SSP245 and SSP585 scenarios, the trends of HWN also increased from 0.31\u0026plusmn;0.01 counts per decade under SSP245 to 0.50\u0026plusmn;0.02 counts per decade under SSP585 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01,\u0026nbsp;Fig. 6a, c).\u003c/p\u003e\n\u003cp\u003eThe increase in the atmospheric water-holding capacity associated with a temperature increase (the Clausius\u0026ndash;Clapeyron relation) considerably influences the changes in extreme precipitation intensity at a rate of ~7%/℃ under warmer climates (Pall et al. 2007, Allan and Soden 2008, Utsumi et al. 2011). The increase in AEP and HWT reaches 0.14\u0026plusmn;0.03\u0026nbsp;mm per decade and 2.61\u0026plusmn;0.10\u0026nbsp;days per decade under the SSP245 scenario, under SSP585 scenario, the frequency of AEP and HWT rises at a rate of 0.37\u0026plusmn;0.03\u0026nbsp;mm per decade and 7.77\u0026plusmn;0.21\u0026nbsp;days per decade (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Fig. 6b, d). Notably, the trends of HWT and AEP under the SSP585 are 3.0-fold and 2.6-fold higher than those of SSP245, respectively. While the trends of HWN and EPE under the SSP585 are 1.6-fold and 2.3-fold higher than those of SSP245, respectively. This suggests that the intensity of HWs and EPEs changes faster than the frequency.\u003c/p\u003e\n\u003cp\u003eThe HWs are becoming more frequent, longer lasting, and more intense in eastern China under global warming background (Fig. S7). On the other hand, global warming will alter atmospheric circulation and evaporation in some regions, providing a richer source of water for precipitation (IPCC 2018). The synergy of these two factors increases the probability of EPEs in humid regions (Allan and Soden 2008) (Fig. S8). Summer Tasmax anomalies in eastern China are shown to increase by to 1 ℃ in 2020 and are projected to be 2.5 ℃ and 4.8 ℃ warmer under SSP245 and SSP585 scenarios than the period of 1850-1900 (Fig. S9). Consequently, the Yangtze River Basin and its southern regions will face the combined disasters of HWs and EPEs in summer in the future projections, becoming a regional climate change hotspot.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Underlying mechanisms and connections between HWs and EPEs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropogenic factors contributes to the rising occurrence of heavy precipitation and high-temperature extremes globally (Fischer and Knutti 2015, Dosio et al. 2018). The rapid increase in the risk of summer HWs and EPEs in easter China can also be partially attributed to global warming (Sun et al. 2014, Liu et al. 2015, Freychet et al. 2017). Furthermore,\u0026nbsp;SST anomaly (SSTA) plays an important role in modulating HWs and EPEs in China (Zhu et al. 2011, Wang et al. 2017, Wei et al. 2020). When the SSTAs\u0026nbsp;in the Indian Ocean\u0026nbsp;and Tropical North Atlantic are positive, they contribute to the positive rainfall anomalies in South China and the southeast coast (Fig. 7c, d). The long-term climate variations in China in summer may be related to the warming trend of SST in the Indian Ocean (Hu et al. 2003) and teleconnections from the North Atlantic (Shang et al. 2020). Previous studies have shown that summer precipitation over central eastern and southern China are attributed to the warming trend of the ENSO-like SSTAs in the tropical Pacific, Indian Ocean, and North Atlantic, which will trigger anomalous anticyclonic circulation over Philippine Sea (Yang and Lau, 2004, Weijing Li et al. 2018, J. Liu et al. 2019).\u003c/p\u003e\n\u003cp\u003eWhen the SSTAs in the west Pacific, Tropical North Atlantic, and the Indian Ocean are positive, they can cause widespread warming in eastern China (Fig. 7e, g, h). The summer three-ocean SSTAs have a greater influence on Tasmax than on precipitation in eastern China, but the influences have significant variations from region to region. For example, the influence of Indian Ocean SST on precipitation and maximum temperature in eastern China is opposite (Fig. 7c, g). Notably, the west Pacific and Ni\u0026ntilde;o 3.4 SST anomalies alone have little influence on the summer precipitation in eastern China (Fig. 7a, b, f), which confirms that tropical Indian Ocean SST warming acts like a capacitor affecting summer climate anomalies over the Indo-western Pacific and East Asia, and the three-ocean interactions through ocean-atmosphere coupling can modulate climate variability (Xie et al. 2009, Cai et al. 2019, Wang 2019).\u003c/p\u003e\n\u003cp\u003eMoreover, summer EPEs and HWs in eastern China are closely related to the strength and location of the western North Pacific subtropical high (WNPSH), and their influences vary across space (Zhu et al. 2011, Freychet et al. 2017, Zhang et al. 2017). Summer Tasmax and frequency of HWs have a higher correlation with WNPSH than precipitation and frequency of EPEs (Fig. S10). The high value area of the correlation coefficient between anomalous Tasmax and 500-hPa geopotential height (GPH) appeared in the northern and eastern coasts of China, reaching above 0.6 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), the correlation coefficient between the anomalous frequency of HWs and GPH was mainly range from 0.4 to 0.6 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and the spatial distribution of high value areas is scattered (Fig. S10\u0026nbsp;c, d). Notably, the intensification of the WNPSH is favorable for more summer HWs in eastern China under present climate, and more monsoon rainfall and HWs in future projections (Q. Liu et al. 2019, X. Chen et al. 2020, Li et al. 2021).\u0026nbsp;In addition to WNPSH, other factors such as the tropical cyclones, Pacific Decadal Oscillation/Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation may contribute to the interdecadal or multidecadal variations in the EPEs and HWs in eastern China (Ding et al. 2009, Zhang et al. 2017). Detailed analysis of the impacts of these longer timescale factors on EPEs and HWs is beyond the scope of this manuscript.\u003c/p\u003e\n\u003cp\u003eIntuitively, we expect cooler summers when it rains, while heatwaves often accompany droughts (Trenberth and Shea, 2005). In the historical period, the correlation between mean\u0026nbsp;anomalous\u0026nbsp;precipitation and Tasmax is negative in eastern China, while the regions with the positive correlation area appear in the eastern coast of China under SSP245 scenario and expand rapidly under SSP585 scenario (Fig. 8a, c, e). Climate models suggest that EPEs and HWs will become more common in an anthropogenically warmed climate (IPCC, 2014). The correlations between the frequencies of EPEs and HWs shifted from negative in the historical period to positive under future projections (Fig. 8b, d, f). These results reveal a distinct link between future rainfall and temperature, with increased EPEs significantly associated with HWs during summer (Raghavendra et al. 2019). In addition, summer precipitation and maximum temperature were negatively correlated, consistent with the argument that warmer summers tend to be dryer, but this negative correlation is largely reduced in extreme cases.\u003c/p\u003e\n\u003cp\u003eHWs are strongly linked to global warming, and previous studies have shown a significant increase in global HW activity from present climate to future projections (Hu et al. 2003, Freychet et al. 2017, Dosio et al. 2018, Perkins-Kirkpatrick and Lewis 2020). However, the compound disasters of HWs and EPEs in a warming climate are rarely studied. In eastern China, as the correlations between the frequencies of EPEs and HWs shifts from negative to positive in summer, this will lead to future loss of life and property and enormous socioeconomic consequences. It further supports the urgent need for policymakers to take action to curb greenhouse emissions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eEPEs and HWs can significantly affect the socioeconomic losses and human health risks related to extreme climate events during summer, especially in the densely populated and economically active areas of the East Asian summer monsoon region. This study attempted to investigate the current and future spatiotemporal characteristics of EPEs and HWs in eastern China and the impacts of HWs on EPEs\u0026nbsp;changes in a warming world. The main conclusions based on the above analysis are as follows:\u003c/p\u003e\n\u003cp\u003e1. The regions with the largest EPEs are apparent in the south of the Yangtze River Basin and its western part, with an average of 3\u0026ndash;5 EPEs yr\u003csup\u003e-1\u003c/sup\u003e during 1979\u0026ndash;2019, the regions with large mean AEP appear in the southeast coast, Yangtze River Basin, North China Plain, and the southern coast of Northeast Plain, ranging from 60 to 110 mm. The metrics of HWs all show increase trends over 1979\u0026ndash;2019, and the increased HWN resulted in a rapid increase of HWT, with rates of 0.69\u0026plusmn;0.22 days per decade and 0.12\u0026plusmn;0.03 counts per decade, respectively.\u003c/p\u003e\n\u003cp\u003e2. In terms of spatial variation, the high values of multi-year mean HWT are 20\u0026ndash;30 days and 50\u0026ndash;70 days under SSP245 and SSP585 scenarios, respectively.\u0026nbsp;The increased HWDU and HWN lead to a rapid increase in HWT.\u0026nbsp;However,\u0026nbsp;there is little change in the frequency of EPEs and AEP from SSP245 to SSP585 scenarios. In terms of temporal variation, HWs and EPEs are both increased in future projections. The Yangtze River Basin and its southern regions will face the compound disaster of HWs and EPEs in summer in the future projections.\u003c/p\u003e\n\u003cp\u003e3. High values of the annual mean total person-times affected by EPEs and HWs are observed in the North China Plain, Yangtze River Delta, Sichuan Basin, and southeast coast, with over four million person-times. The total person-times affected by EPEs range between 3.5 billion to 4.5 billion in eastern China shows a slightly decrease trend under both scenarios. The total person-times affected by HWs under SSP245 scenario maximize at around 4.0 billion, lower than the peak person-times (about 5.0 billion) under SSP585 scenario.\u003c/p\u003e\n\u003cp\u003e4. The increased EPEs and HWs are primarily caused by anthropogenic warming. Positive summer SSTs (West Pacific, Tropical North Atlantic, and the Indian Ocean) can cause widespread warming in eastern China and a regional increase in rainfall in the Yangtze River Basin. Moreover, summer Tasmax and frequency of HWs have a higher correlation with WNPSH than precipitation and frequency of EPEs. We detect strong positive correlation between mean-state (Tasmax or Precipitation) and extreme climate (HWs or EPEs), this implies that improved prediction of mean-state climate can provide insights into the extreme prediction. In eastern China, summer precipitation and temperature were negatively correlated, consistent with the argument that warmer summers tend to be dryer, but this negative correlation is largely reduced in extreme cases. As the correlations between the frequencies of EPEs and HWs shifts from negative to positive in summer, this will lead to future loss of life and property and enormous socioeconomic consequences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the datasets used in this study are publicly available. The NOAA High Resolution SST V2.1 data is provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. ERA5 dataset is available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The daily 2-m maximum surface air temperature and precipitation datasets are available at http://data.cma.cn/en. The daily climate model outputs are obtained from https://esgf-node.llnl.gov/projects/cmip6/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science 321: 1481-1484. \u003c/li\u003e\n\u003cli\u003eBao J, Sherwood SC, Alexander LV et al (2017) Future increases in extreme precipitation exceed observed scaling rates. Nat Clim Chang 7: 128-132. \u003c/li\u003e\n\u003cli\u003eBoer G (1993) Climate change and the regulation of the surface moisture and energy budgets. Clim Dyn 8: 225-239. \u003c/li\u003e\n\u003cli\u003eBonsal BR, Zhang X, Vincent LA et al (2001) Characteristics of daily and extreme temperatures over Canada. J Clim 14: 1959-1976. \u003c/li\u003e\n\u003cli\u003eCai W, Wu L, Lengaigne M et al (2019) Pantropical climate interactions. 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Clim Dyn 36: 1463-1473. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Extreme Precipitation, Heatwaves, Eastern China, CMIP6, Climate Projection, SSP Scenarios","lastPublishedDoi":"10.21203/rs.3.rs-2114246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2114246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Observational and modeling analysis suggests an increased frequency of heatwaves and extreme precipitation in an anthropogenically warmed climate. However, the accurate link between extreme precipitation events (EPEs) and heatwaves (HWs), and changes in these extremes and associated socio-economic impacts in eastern China have not been fully resolved. This study examines historical and future changes of summer EPEs and HWs in eastern China based on observations, reanalysis, and model outputs from the Coupled Model Intercomparison Project Phase 6. The results show that EPEs and HWs in eastern China have increased in the past four decades and are projected to rise in the future. The Yangtze River Basin and its southern regions will be confronted with the compound disaster of HWs and EPEs in the future projections. High values of the annual mean total person-times (estimated as population density multiplied by event frequency) affected by EPEs and HWs are observed in the North China Plain, Yangtze River Delta, Sichuan Basin, and southeast coast. The total person-times affected by EPEs show a slightly decreasing trend under both scenarios. However, the total person-times affected by HWs under Shared Socioeconomic Pathway (SSP) 245 scenario maximize at around 4.0 billion, lower than the peak person-times (about 5.0 billion) under SSP585 scenario. We further investigate the linkage of such extreme events with sea surface temperature and western North Pacific subtropical high anomalies. The correlations between the mean-state and extreme precipitation and maximum temperature anomalies both shifted from negative in the historical period to positive under future projections.","manuscriptTitle":"Increasing Impacts of Summer Extreme Precipitation and Heatwaves in Eastern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-10 19:24:55","doi":"10.21203/rs.3.rs-2114246/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2022-10-24T08:28:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-10-05T19:05:34+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-30T08:18:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2022-09-28T23:00:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3508ee41-b266-425c-835d-63f00b046c60","owner":[],"postedDate":"October 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-25T15:02:08+00:00","versionOfRecord":{"articleIdentity":"rs-2114246","link":"https://doi.org/10.1007/s10584-023-03610-4","journal":{"identity":"climatic-change","isVorOnly":false,"title":"Climatic Change"},"publishedOn":"2023-09-19 15:00:31","publishedOnDateReadable":"September 19th, 2023"},"versionCreatedAt":"2022-10-10 19:24:55","video":"","vorDoi":"10.1007/s10584-023-03610-4","vorDoiUrl":"https://doi.org/10.1007/s10584-023-03610-4","workflowStages":[]},"version":"v1","identity":"rs-2114246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2114246","identity":"rs-2114246","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}