Natural Reserves Exposure to Rainstorm in the Eastern Monsoon Region of China

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The establishment of natural reserves does not perfectly protect rare animal species from the adverse effects of extreme rainfall. Developing a risk assessment system for heavy rainfall exposure in natural reserves can identify those at heightened risk during precipitation events. This research utilizes observational data from meteorological stations from 1971 to 2020 to reveal spatiotemporal trends of heavy rainfall in the eastern monsoon region of China and establishes an exposure risk assessment framework to evaluate future scenario risks for natural reserves. Results indicate that the annual average number of heavy rainfall days gradually increases from northwest to southeast, displaying a distinct zonal distribution pattern. The proportion of heavy rainfall days to total precipitation days and the average intensity of heavy rainfall show peak centers in the southeastern coastal areas, western Sichuan region, and North China Plain, with minimum values observed in the northwestern direction. Protected areas in China's Eastern Monsoon Region display a north-south gradient of precipitation exposure risk that intensifies from near (2031–2050) to far (2081–2100) future under SSP245 scenario, with highest vulnerability in southeastern coastal areas. National reserves generally experience lower exposure than provincial and municipal ones, though all categories face increasing precipitation risks over time. Eastern monsoon region Rainstorm Spatiotemporal distribution Natural reserves Biodiversity CMIP6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Against the backdrop of global warming, significantly increased atmospheric water vapor content has accelerated the hydrological cycle, causing heavy rainfall events to exhibit characteristics of increased frequency and intensity (Myhre, Alterskjær et al., 2019 ,Tabari, 2020 ). The IPCC Sixth Assessment Report clearly states that for each 1°C increase in global warming, extreme daily precipitation intensity will increase by approximately 7%. The gradual escalation in rainfall intensity and frequency is threatening biodiversity by altering ecosystem structure and function (Pawson, Brin et al., 2013 ) attracting widespread attention from scholars worldwide. Climate change and anthropogenic pressure represent two major global challenges that pose enormous threats to biodiversity (Wan, Jiang et al., 2019 ). Heavy rainfall poses significant risks to nature reserves through both direct and indirect impacts, affecting comprehensive management, protected wildlife, and their habitats. In the eastern monsoon region of China, heavy rainfall often triggers a range of secondary disasters such as flash floods, landslides, and debris flows. These occur when intense precipitation rapidly saturates the soil, increases surface runoff, and destabilizes slopes, especially in mountainous and hilly areas common to many nature reserves. As a result, nature reserves in this region face significant challenges in comprehensive management. Monitoring instruments are frequently damaged or rendered inoperable due to flooding or power disruptions, undermining the continuity and accuracy of long-term ecological data collection. Infrastructure such as patrol roads is highly vulnerable to washouts and collapses, impeding routine inspections and delaying emergency response efforts—sometimes isolating entire sections of a reserve. Due to climate change, most species habitats demonstrate trends of reduction or stabilization, with animal and plant habitats shifting from integration toward fragmentation (Yuan, Zhang et al., 2024 ). Continuous heavy rainfall creates conditions for the outbreak of animal infectious diseases, reduces vegetation quality for herbivores, and even alters herbivores' spatial utilization patterns, including migration (Bartzke, Ogutu et al., 2018 ). Extreme precipitation causes extensive damage to coastal tree species (Kramer, Vreugdenhil et al., 2008), while disasters such as floods caused by heavy rainfall result in mass mortality of species within wetland ecosystems (Vretare, Weisner et al., 2001 ,Asaeda and Hung, 2007 ). Identifying habitats severely impacted by heavy rainfall and implementing protective measures represents a crucial issue that needs to be addressed currently. China's eastern monsoon region is a key area for biodiversity conservation. This region not only nurtures rich wildlife resources (Wen, Gu et al., 2015 ),but also faces severe challenges from extreme precipitation events (Shrestha, Xu et al., 2021 ). As a sensitive area to global climate change, China's eastern monsoon region is under dual pressure from monsoon circulation anomalies and regional warming, coupled with topographic uplift effects, making it a hotspot for intense precipitation events (Wang and Zhou, 2005 ,Dong, Noyelle et al., 2024 ). Extreme precipitation events have increased significantly in East China, especially in the Yangtze River Basin, with a trend of 10–20% per decade in summer (Wang and Zhou, 2005 ). Particularly during the summer monsoon, the frequent convergence of warm and humid airflows from the tropical western Pacific and Indian Oceans with polar cold air, combined with factors such as topographic uplift, leads to frequent regional heavy precipitation events (Dong, Noyelle et al., 2024 ). Protected Areas (PA) are globally recognized strategies for addressing the survival crisis of rare animals (Hoekstra, Boucher et al., 2005 ,Geldmann, Joppa et al., 2014 ,Lewis, MacSharry et al., 2019 ), playing a crucial role in reducing habitat loss and maintaining sustainable population levels of species (Gray, Hill et al., 2016 ). To better protect rare flora and fauna, China has established natural reserves to provide habitats for wild animals and plants (Wang, Feng et al., 2020 ). However, the current system of protected areas has limitations in responding to precipitation changes and struggles to maintain biodiversity conservation value (Xu and Wu, 2024 ). It is necessary to identify those with higher rainfall risks and develop relevant response mechanisms. Therefore, approaching from the perspective of heavy rainfall disasters at a more macro scale to systematically evaluate the exposure risks of natural reserves in China's eastern monsoon region has constructive significance for improving China's natural reserve system. Existing related studies mostly focus on specific areas within a certain protected area or on individual heavy rainfall events (Wang, Feng et al., 2020 ,Liu, Yuan et al., 2022 ), with small spatial scales and short time spans, lacking a macro-level portrayal of natural reserves affected by heavy rainfall over large areas and extended time periods. Based on this, our study selects China's eastern monsoon region as the research area. The study defines heavy rainfall events as daily precipitation exceeding 50 mm and systematically analyze the spatiotemporal evolution characteristics of heavy rainfall events in China's eastern monsoon region from 1971 to 2020. The study assesses nature reserves’ exposure to heavy rainfall disasters and predict exposure risks for 2031–2050 as the near future and 2081–2100 as the far future based on multi-model ensemble simulation data from CMIP6. This research not only helps deepen the understanding of regional extreme precipitation event evolution patterns but also provides scientific basis for adaptive conservation management of rare wildlife. By constructing a heavy rainfall risk assessment system on a larger spatiotemporal scale, this study offers reference for improving extreme precipitation prevention and protection measures in China's eastern monsoon region. It provides scientific foundation and reference for future guidance on disaster prevention and mitigation in protected areas and ensuring biodiversity security, with important theoretical and practical significance for enhancing regional biodiversity conservation capacity. 2. Materials and Methods 2.1 Study Area China's eastern monsoon region is one of the most typical and unique monsoon regions in the global climate system, spanning a vast geographical area in eastern China with significant climatic, geographical, and ecological characteristics. This region roughly encompasses the eastern Yangtze River, and areas north of the Qinling Mountains-Huaihe River line, including East China, Central China, and parts of North China. It covers part or all of provinces such as Jiangsu, Zhejiang, Shanghai, Anhui, Jiangxi, Hubei, Hunan, Fujian, and Guangdong. The region is densely populated, economically developed, and is also one of the most biodiverse regions in China.The eastern monsoon region is significantly influenced by the East Asian monsoon climate system, exhibiting distinct monsoon climate characteristics. It is one of China's most biodiverse regions, home to numerous national-level natural reserves. These protected areas provide habitats for rare and endangered species such as giant pandas, Père David's deer, and white cranes, and contain extremely fragile ecosystems. The complex topography, abundant precipitation, and unique ecological environment of the region have created this treasure trove of biodiversity. 2.2 Data We utilize rainfall data from meteorological stations measured from 1971 to 2020 as historical rainfall observation data, and analyze future rainfall scenarios under SSP2-4.5 from CMIP6 (the Coupled Model Intercomparison Program in Phase 6). We reveal both the spatiotemporal distribution of historical rainfall in the eastern monsoon region and the exposure risks to natural reserves under historical and different future scenarios. Additionally, this paper uses point data of ecological functional zones of natural reserves from ArcGIS online China, with a total of 2005 points located in the eastern monsoon region, including 258 national-level, 563 provincial-level, and 258 city/county-level reserves. It should be noted that, due to the high fragmentation of nature reserves in the eastern monsoon region (Zhao, Zong et al., 2024 ), these reserves are characterized by a large number and small size. To better integrate with the gridded data of storms, this study adopted point - based data for the nature reserves. Table 1 Data Sources Data Name Time Span Spatial resolution Time resolution Source Precipitation Data 1971–2020 Meteorological Station Daily China Meteorological Administration ACCESS-CM2 2015–2100 1.25° × 1.25° Daily Earth System Grid Federation ACCESS-ESM1-5 2015–2100 1.25° × 1.25° Daily Earth System Grid Federation BCC-CSM2-MR 2015–2100 1.125°×1.125° Daily Earth System Grid Federation CanESM5 2015–2100 2.8° × 2.8° Daily Earth System Grid Federation EC-EARTH3-VEG 2015–2100 0.25° × 0.25° Daily Earth System Grid Federation GFDL-ESM4 2015–2100 1° × 1° Daily Earth System Grid Federation IPSL-CM6A-LR 2015–2100 2.5° × 1.25° Daily Earth System Grid Federation MIROC6 2015–2100 1.4° × 1.4° Daily Earth System Grid Federation MPI-ESM1-2-HR 2015–2100 0.5° × 0.5° Daily Earth System Grid Federation MPI-ESM1-2-LR 2015–2100 1.9° × 1.9° Daily Earth System Grid Federation 2.3 Methodology The technical approach of this research is primarily divided into two parts: First, we explore the spatiotemporal variation characteristics of heavy rainfall events in the eastern monsoon region over the 50-year period from 1971 to 2020. Then, based on future precipitation data from individual CMIP6 projection models, we calculate the exposure risk of rare animals to heavy rainfall disasters for both near- and long-term future periods by averaging the results obtained from each model separately. All calculations are implemented in Python and ArcGIS. Specifically, this study adopts the definition of extreme rainfall established by the China Meteorological Administration, which classifies a rainfall event as extreme if the daily precipitation exceeds 50 mm. For the multi-model CMIP6 ensemble, all datasets were resampled to match the spatial resolution and extent of the BCC-CSM2-MR model, after which an ensemble mean was calculated to derive the final future projection. For individual CMIP6 models, data from 2015 to 2025 were used as the historical baseline to compute the mean and standard deviation. These parameters were then applied to both near-future and far-future scenarios. Accordingly, the exposure risk under future scenarios assessed in this study represents a relative risk in comparison to the historical baseline. 2.3.1 Exposure Risk Caculation The assessment of exposure risk for rare animals is the core component of this research, and its methodological design must balance scientific validity and systematic approach. This study constructs a multi-dimensional, multi-indicator exposure risk assessment system. The central approach is to eliminate dimensional differences between indicators through standardization and objectively determine the weight of each indicator using information entropy weighting methods. Specifically, we define heavy rainfall intensity as the annual total heavy rainfall and use its standardized value as the intensity indicator; we use the standardized annual heavy rainfall frequency as the frequency indicator; and the average of the standardized Mann-Kendall trend values of both as the trend indicator. After determining the weights for these three indicators, they are summed to obtain the final exposure degree. In the final stage of exposure risk assessment, the calculated heavy rainfall hazard indicators are overlaid with spatial data of natural reserves for rare animals. 2.3.2 Rainstorm Center Calculation This research uses Chinese climate zones as grouping fields, dividing the stations in the eastern monsoon region into tropical/subtropical and temperate categories, with geographical location as the foundation. Using the annual average number of heavy rainfall days and annual average heavy rainfall intensity from the 1971–1980 and 2011–2020 time periods as weighting fields, we calculate the central locations of average heavy rainfall days and average heavy rainfall intensity over 50 years (Eq. 1 ). Based on changes in these central locations, we can observe and analyze the migration characteristics and trends of heavy rainfall centers in different climate regions of the eastern monsoon region. Where x i and y i are the coordinates of feature i, and w i is the weight at feature i. This research uses Chinese climate zones as grouping fields, dividing the stations in the eastern monsoon region into tropical/subtropical and temperate categories, with geographical location as the foundation. Using the annual average number of heavy rainfall days and annual average heavy rainfall intensity from the 1971–1980 and 2011–2020 time periods as weighting fields, we calculate the central locations of average heavy rainfall days and average heavy rainfall intensity over 50 years. Based on changes in these central locations, we can observe and analyze the migration characteristics and trends of heavy rainfall centers in different climate regions of the eastern monsoon region. 2.3.3 Other methods We employ the Mann-Kendall test to reveal interannual trends in heavy rainfall. When constructing the exposure risk assessment system, we use the Entropy Weight Method to eliminate the subjectivity of artificial weighting, assigning weights to various factors in the risk assessment system and summing them to obtain a comprehensive risk measurement. The K-Means clustering algorithm is used to classify the risks in the eastern monsoon region into five categories: low risk, relatively low risk, medium risk, relatively high risk, and high risk areas. 3. Result 3.1 Spatiotemporal Distribution Characteristics of Heavy Rainfall in China's Eastern Monsoon Region from 1971 to 2020 Heavy rainfall characteristics in China's eastern monsoon region exhibit complex and significantly differentiated spatial distribution patterns (Fig. 2 ). The regional variation in annual average heavy rainfall days demonstrates notable geographic gradient characteristics (Fig. 2 a), showing a distinct increasing trend from northwest to southeast. Coastal areas and southern regions have significantly higher numbers of heavy rainfall days than inland and northern regions, a distribution pattern closely related to regional topography, land-sea effects, and monsoon circulation. Specifically, the southernmost regions reach peak values in heavy rainfall days, while the northwestern, northeastern, and southwestern marginal areas have relatively fewer heavy rainfall days.The proportion of heavy rainfall volume, an important indicator measuring the contribution of heavy rainfall to regional precipitation (Fig. 2 b), also presents significant spatial differences. The heavy rainfall proportion in the northeast region is only 2%-8%, while regions south of the Yangtze River have proportions as high as 20%-30%, highlighting the enormous difference in precipitation characteristics between north and south.The average intensity of heavy rainfall likewise exhibits significant spatial heterogeneity across the eastern monsoon region (Fig. 2 c). Overall, it shows characteristics of greater intensity in the east than in the west and greater in the south than in the north, with intensity decreasing along the northwest to southeast direction. Maximum values are concentrated in the southernmost areas, with central and eastern regions also showing areas of relatively high intensity. Notably, the eastern edge of the Sichuan-Chongqing region in the western part of the study area exhibits abnormally high heavy rainfall intensity. The average centers of heavy rainfall days and intensity were calculated for stations in tropical/subtropical and temperate regions for the two time periods 1971–1980 and 2011–2020 (Fig. 3 ). It can be observed that from the 1970s to the 2010s, the centers of heavy rainfall intensity and days in both tropical/subtropical monsoon regions and temperate monsoon regions show a trend of movement toward the northeast direction. Specifically, the center of heavy rainfall days in the tropical/subtropical monsoon region moved 52.9km in a north-northeast direction (46.6° east of north); the center of heavy rainfall intensity in the tropical/subtropical monsoon region moved 20.6km in a north-northeast direction (47.5° east of north); the center of heavy rainfall days in the temperate monsoon region moved 81.4km in a north-northeast direction (62.6° east of north); and the center of heavy rainfall intensity in the temperate monsoon region moved 71.3km in a north-northeast direction (63.2° east of north). Additionally, it can be seen that the movement angles and distances of the two centers in the temperate zone are more pronounced.It can be observed that the overall migration distance in temperate regions is greater than that in tropical and subtropical regions, indicating that the intensity of strengthening rainfall in the northern parts of temperate regions is greater than in tropical and subtropical regions. Table 2 Migration distances of heavy rainfall centers Area Category Distance(km) Angle tropical and subtropical center of days of the rainstorm 52.9 46.6° north by east center of intensity of the rainstorm 20.6 47.5° north by east temperate center of days of the rainstorm 81.4 62.6° north by east center of intensity of the rainstorm 71.3 63.2° north by east The frequency of heavy rainfall events within the eastern monsoon region, the proportion of frequency among total rainfall events, and intensity all exhibit obvious zonal differences (Fig. 4 ). In tropical and subtropical regions, the frequency and intensity of heavy rainfall events show a steady upward trend and are significantly higher than in temperate regions. In temperate regions, there is no obvious trend in heavy rainfall frequency and intensity over the 50-year period, but there are large fluctuations around the mean value. The proportion of heavy rainfall frequency to total annual rainfall events in tropical and subtropical regions also shows a clear upward trend, while temperate regions show a smaller upward trend. Notably, around 1996, the two regions converged very closely, while in other years, the tropical and subtropical regions were distinctly higher than the temperate regions. 3.2 Heavy Rainfall Exposure Risk of Natural Reserves in China's Eastern Monsoon Region The spatial distribution of extreme precipitation hazards in China's Eastern Monsoon Region under SSP245 scenario shows distinctive patterns between near and far future periods. In the near-future (Fig. 5 a), a clear north-south gradient is observed with lower hazard values predominating the northern portion of the monsoon region while higher hazard levels concentrate in the southeastern coastal areas. The far-future projection (Fig. 5 b) maintains this fundamental spatial structure but demonstrates significant intensification of hazards, particularly in the southeastern coastal provinces where values approach the maximum of 2.663. Both temporal horizons consistently show a transition zone between low and high precipitation hazards approximately along the mid-latitudes of the Eastern Monsoon Region. The most substantial change between the two periods occurs in the southeastern coastal areas, where moderate hazards in the near future evolve into severe hazards by century's end, indicating an amplification of the monsoon-driven precipitation extremes under continued warming. This pattern highlights the differential vulnerability within the Eastern Monsoon Region, with southern provinces facing increasingly severe precipitation hazards while northern areas experience relatively modest changes in extreme precipitation intensity between the two time periods. The spatial distribution of natural protected areas' exposure to extreme precipitation hazards in China's Eastern Monsoon Region demonstrates clear patterns across different risk levels. In the near-future scenario (Fig,6a), protected areas exhibit a distinctive north-south gradient of exposure, with northern areas predominantly showing minimal to low exposure levels, while southeastern coastal regions display high to extreme exposure levels. The far-future projection (Fig,6b) maintains this spatial pattern but shows an intensification of exposure in the southeastern coastal protected areas, with more sites shifting to extreme exposure categories. Both time periods maintain consistent minimal exposure in northern protected areas, while the transition zone between low and high exposure follows approximately the middle latitudes of the Eastern Monsoon Region. The most notable change between the two periods occurs in the southeastern coastal areas, where moderately exposed protected areas in the near future transition to high or extreme exposure by century's end, indicating increasing vulnerability of conservation areas to precipitation extremes under continued warming in the SSP245 pathway. The analysis of precipitation exposure risk across different levels of protected natural reserves in China's Eastern Monsoon Region reveals distinct patterns of vulnerability. In national-level reserves (Fig. 7 a), there is a declining trend of exposure from low to high risk levels in the near future, with approximately 32% of reserves at risk level 1 and only about 14% at level 5. However, in the far future scenario, the distribution becomes more balanced with notable increases in reserves facing level 2 risk. Provincial-level reserves (Fig. 7 b) show a similar pattern to national reserves in the near future, with higher proportions at lower risk levels, but display a more pronounced shift toward higher risk levels in the far future projection. Notably, about 37% of provincial reserves face level 1 risk in the near future, which decreases in the far future scenario, indicating a transition toward moderate and higher risk categories. Municipal-level reserves (Fig. 7 c) exhibit a distinctly different pattern, with a bimodal distribution showing peaks at both the lowest and highest risk categories in both time periods. This suggests municipal reserves are more polarized in their exposure risks compared to national and provincial reserves. Additionally, municipal reserves show the most substantial increase in high-risk exposure from near to far future scenarios. Comparing across administrative levels, national reserves appear to maintain relatively better protection from extreme precipitation risks over time compared to provincial and municipal reserves, which show progressively higher vulnerability to extreme risk levels in future scenarios. This hierarchical pattern suggests that higher administrative-level protected areas may have been established in locations inherently less vulnerable to precipitation extremes, or may have better adaptive management strategies in place. 4. Discussion 4.1 The Methodology for Constructing a Rainstorm Exposure Risk Assessment System In the field of risk assessment, understanding the world in relation to risks and how we can and should understand, evaluate, and manage these risks is fundamental. In FEMA's (National Risk Index), hazard exposure is defined as the representative value of buildings (in dollars), population (in numbers and population-equivalent dollars), or agriculture (in dollars) that may be exposed to natural hazard events. Exposure assessments are conducted to identify areas most vulnerable to the effects of heavy rainfall, as well as potential casualties, property damage, and economic losses that may result from heavy rainfall events. Some studies have also selected land use and rainfall intensity as evaluation factors, basing heavy rainfall disaster exposure assessment on both the probability of being affected by heavy rainfall and potential losses (Simin and Shaobo, 2015 ). This study constructs a heavy rainfall exposure risk assessment system primarily based on the frequency of heavy rainfall events and annual heavy rainfall intensity as evaluation factors. After calculating the relevant values of these two evaluation factors for each grid cell within the corresponding research time period, the entropy weight method is used to calculate the weights of these two factors, which are then summed to obtain the hazard distribution. On this basis, the hazard distribution is further overlaid with natural reserves and classified using the k-means clustering method for assessment.Conventional exposure calculation methods typically multiply the disaster frequency within the study period by the carrier area, population, or total GDP value of the region (Tabari, 2020 ). However, due to the special nature of the disaster-bearing bodies in this study, we cannot accurately quantify the number of various rare animals within natural reserves. Furthermore, since the area of protected reserves does not have a direct relationship with the disaster situation of rare animals within them, using reserve area directly is not entirely appropriate. Therefore, we adopt a direct overlay approach for statistical analysis. 4.2 Consistency Between the Evaluation Results of Medium-High Risk Areas and Actual Conditions In China's eastern monsoon region, the annual average number of heavy rainfall days and intensity generally shows an increasing pattern from northwest to southeast, while the proportion of heavy rainfall is higher in Sichuan and central regions. In the future, China will generally experience an increase in heavy rainfall days, especially in the northeastern regions such as Jilin and Heilongjiang, eastern coastal areas, and parts of Jiangxi and Hunan. From the overall northward migration of heavy rainfall centers, it can be observed that as heavy rainfall increases across the eastern monsoon region, the threat of rainfall growth in northern parts of each region exceeds that in southern parts, consistent with research findings by scholars (LI and ZHAO, 2022 ,HU, DONG et al., 2024 ). Combining historical and future heavy rainfall hazards, it can be found that the overall risk for natural reserves in the eastern monsoon region shows a trend of being safer in the northwest direction and more dangerous in the southeast direction, indicating that historical data calculations and CMIP6 prediction results are relatively coherent and comparable. Both historical and future hazard results show that the risk in central China is relatively larger compared to surrounding areas, which is related to the large proportion of heavy rainfall in total precipitation in this region. The results also show that, relatively speaking, historical data is more sensitive in the southernmost coastal areas, while the locations of extreme hazards in future predictions are in central China. This may be due to the large differences in historical heavy rainfall intensity, while the CMIP6 dataset's ability to predict extreme heavy rainfall is insufficient (Dong and Dong, 2021 ), weakening the impact of extremely heavy rainfall intensity in coastal areas of Hainan and Guangdong provinces. In both the near and distant future, the risk to protected areas in central and southern China remains high, with risks in the near future generally lower than in the distant future. Among all provinces, Guangdong faces the highest current risk for rare animals. Species such as the macaques on Shangchuan Island and the sika deer on Dazhang Island (Zhang, 1985 )are under significant threat, which aligns with actual findings (Zheng, Huang et al., 2016 ,Daily, 2020 );The risk to rare animals in Guangxi Zhuang Autonomous Region and Hainan Province is somewhat lower, while rare animals in Gansu Province, Shanxi Province, and Inner Mongolia Autonomous Region are relatively safe. In Guangdong, Guangxi, and Hainan, natural reserves need to improve emergency response mechanisms for heavy rainfall, providing facilities such as shelters for rare animals to rest and seek refuge from the rain to ensure their safety. In the future, natural reserves along the Yangtze River in Central China, particularly in Hunan, Hubei, and Jiangxi, as well as the southwestern regions further west (Yang, Gao et al., 2019 ,Wang, Cheng et al., 2023 ), will face even greater threats from heavy rainfall. These areas have been severely affected by heavy rainfall disasters in recent years and require enhanced preventive measures. 5. Conclusions This study finds that heavy rainfall exposure in China’s Eastern Monsoon Region shows a clear spatial pattern, increasing from northwest to southeast, with peak intensity in southeastern coastal areas, the western Sichuan region, and the North China Plain. Under the SSP245 scenario, precipitation exposure risk in natural reserves is projected to intensify over time (from 2031–2050 to 2081–2100), especially in the southeast, forming a pronounced north-south gradient. Although national reserves generally face lower exposure than provincial and municipal ones, all levels will experience rising risks. These results highlight the need for climate-adaptive conservation planning to protect vulnerable reserves from future extreme rainfall events. Declarations Acknowledgments This research was funded by Third Xinjiang Scientific Expedition Program (2022xjkk0601), National Natural Science Foundation of China (42471085 and U22B2011), Natural Science Foundation of Hubei Province (2023AFB823). Availability Statement The future rainfall data used in this study are publicly available from the Earth System Grid Federation (ESGF) website at https://aims2.llnl.gov/search/cmip6/ . The historical site-measured rainfall data that support the findings of this study are available from the corresponding author, Sun Shao from the Chinese Academy of Meteorological Sciences, upon reasonable request. The nature reserve sites analyzed in this study and detailed data can be found at https://mp.weixin.qq.com/s/YtfrODTKUwPFoibTrGFm1Q . References Asaeda T, Hung LQ (2007) Internal heterogeneity of ramet and flower densities of Typha angustifolia near the boundary of the stand. Wetlands Ecol Manage 15:155–164 Bartzke GS, Ogutu JO, Mukhopadhyay S, Mtui D, Dublin HT, Piepho H-P (2018) Rainfall trends and variation in the Maasai Mara ecosystem and their implications for animal population and biodiversity dynamics. PLoS ONE 13(9):e0202814 Daily NM (2020) Jiangmen Taishan Shangchuan Island suffered heavy rainfall and flooded streets, residents said they have long been accustomed to it. from https://static.nfapp.southcn.com/apptpl/videoToShare/index.html?id=3561372#/ Dong C, Noyelle R, Messori G, Gualandi A, Fery L, Yiou P, Vrac M, D’andrea F, Camargo SJ, Coppola E (2024) Indo-Pacific regional extremes aggravated by changes in tropical weather patterns. Nat Geosci 17(10):979–986 Dong TY, Dong WJ (2021) Evaluation of extreme precipitation over Asia in CMIP6 models. Clim Dyn 57(7–8):1751–1769. 10.1007/s00382-021-05773-1 Geldmann J, Joppa LN, Burgess ND (2014) Mapping Change in Human Pressure Globally on Land and within Protected Areas. Conserv Biol 28(6):1604–1616 Gray CL, Hill SLL, Newbold T, Hudson LN, Borger L, Contu S, Hoskins AJ, Ferrier S, Purvis A, Scharlemann JPW (2016) Local biodiversity is higher inside than outside terrestrial protected areas worldwide. NATURE COMMUNICATIONS 7 Hoekstra JM, Boucher TM, Ricketts TH, Roberts C (2005) Confronting a biome crisis: global disparities of habitat loss and protection. Ecol Lett 8(1):23–29 HU J, DONG S, Leibin W, LI W and Q. LI (2024) Impact of Human Activities on Seasonal Changes in Precipitation in China. Clim Environ Res 29(6):643–655. 10.3878/j.issn.1006-9585.2024.23103 Kramer K, Vreugdenhil SJ (2008) Effects of flooding on the recruitment, damage and mortality of riparian tree species: A field and simulation study on the Rhine floodplain. For Ecol Manag 255(11):3893–3903Van Der Lewis E, MacSharry B, Juffe-Bignoli D, Harris N, Burrows G, Kingston N, Burgess ND (2019) Dynamics in the global protected-area estate since 2004. Conserv Biol 33(3):570–579 LI Y, ZHAO S-S (2022) Floods losses and hazards in China from 2001 to 2020. Clim Change Res 18(2):154–165. 10.12006/j.issn.1673-1719.2021.196 Liu M, Yuan H, Zhou S (2022) Influence of Climate Change on Wolong Nature Reserve. FOREST SCIENCE AND TECHNOLOGY(08. 79–82. 10.13456/j.cnki.lykt.2021.08.12.0005 Myhre G, Alterskjær K, Stjern CW, Hodnebrog Ø, Marelle L, Samset BH, Sillmann J, Schaller N, Fischer E, Schulz M (2019) Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep 9(1):16063 Pawson SM, Brin A, Brockerhoff EG, Lamb D, Payn TW, Paquette A, Parrotta J (2013) Plantation forests, climate change and biodiversity. Biodivers Conserv 22:1203–1227 Shrestha N, Xu X, Meng J, Wang Z (2021) Vulnerabilities of protected lands in the face of climate and human footprint changes. Nat Commun 12(1):1632 Simin Y, Shaobo Z (2015) Exposure Assessment of Rainstorm Disaster Based on Land Use and Precipitation Extreme. A Case Study of Beijing, China. Tabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10(1):13768 Vretare V, Weisner SE, Strand JA, Granéli W (2001) Phenotypic plasticity in Phragmites australis as a functional response to water depth. Aquat Bot 69(2–4):127–145 Wan X, Jiang G, Yan C, He F, Wen R, Gu J, Li X, Ma J, Stenseth NC, Zhang Z (2019) Historical records reveal the distinctive associations of human disturbance and extreme climate change with local extinction of mammals. Proceedings of the National Academy of Sciences 116(38): 19001–19008 Wang P, Cheng Q, Jin H (2023) Divergent vegetation variation and the response to extreme climate events in the National Nature Reserves in Southwest China, 1961–2019. Ecol Ind 150. https://doi.org/10.1016/j.ecolind.2023.110247 . 110247.DOI Wang W, Feng C, Liu F, Li J (2020) Biodiversity conservation in China: A review of recent studies and practices. Environ Sci Ecotechnology 2:100025 Wang Y, Zhou L (2005) Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation. Geophys Res Lett 32(9) Wen C, Gu L, Wang H, Lv Z, Hu R, Zhong J (2015) GAP analysis on national nature reserves in China based on the distribution of endangered species. Biodivers Sci 23(5):591–600 Xu B, Wu X (2024) A comprehensive analysis to optimizing national-scale protected area systems under climate change. J Environ Manage 363:121408 Yang X, Gao D, Wang P (2019) Characteristics of Temperature and Precipitation Changes in Wumeng Mountain Natural Reserve in 57 Years. IOP Conference Series: Earth and Environmental Science 237(2): 022013. 10.1088/1755-1315/237/2/022013 Yuan R, Zhang N, Zhang Q (2024) The impact of habitat loss and fragmentation on biodiversity in global protected areas. Sci Total Environ 931:173004 Zhang T (1985) A rare animal breeding experimental base was established on Daman Island. Chin J Wildl 0249DOI. 10.19711/j.cnki.issn2310-1490.1985.02.023 Zhao W, Zong L, Wang M (2024) Spatial distribution of nature reserves in China. Acta Ecol Sin 44(07):2786–2799. 10.20103/j.stxb.202212103552 Zheng W, Huang X, Xu B (2016) Analysis on the characteristics and impact of meteorological disasters in Jiangmen City. Guangdong Meteorol Retrieved 02:38 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 17 Jul, 2025 Editor invited by journal 16 Jul, 2025 Editor assigned by journal 13 Jun, 2025 First submitted to journal 13 Jun, 2025 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. 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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-6888549","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486971085,"identity":"22126317-37b3-4e99-8ef5-e157178601ec","order_by":0,"name":"Hanming Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3RsUoDMRjA8YTAHUKOrDmEPsMngYIo7askHHQsHF1ucIgcpJvzFYrPIAgVt5SAXfIAhS52dzgQFHFoe656x40O+Q9fIOTHNwShUOgfxghZ72u4puMSa6gLJJtb3kXSuckELyYDiInOK9+DgPdDTr0TwGL9fmZ6ELSVwFND1BPBt4/43k3BkvWOotG0TeBKSshNpJ5LXIp85WZgo+yKomzWRgiXVqaGKu2wEYvVTj1YOjynyCrdQiKutE0Mb8j8O1k2hH10Ekod1tSDgNMpEv2zJeokPDaE8EIO0tO/XFQvB7VwkbhcQtZKxo59ftVwoIxtXqG+mai7TbnfvhWjVvJHpBnQ/30oFAqFfncEFptfD39LeFcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0003-9993-7302","institution":"Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Hanming","middleName":"","lastName":"Cao","suffix":""},{"id":486971086,"identity":"e6625fb8-f995-47e7-b71b-4861b366e84a","order_by":1,"name":"Yixuan Zhou","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Zhou","suffix":""},{"id":486971087,"identity":"82a76071-cd2b-4a01-a93b-cf8e5e9bab49","order_by":2,"name":"Lin Zhao","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhao","suffix":""},{"id":486971088,"identity":"0d8d428c-c7f2-4926-90f7-b8b9fb8954ca","order_by":3,"name":"Sun Shao","email":"","orcid":"","institution":"China Academy of Meteorological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Shao","suffix":""}],"badges":[],"createdAt":"2025-06-13 13:17:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6888549/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6888549/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87332353,"identity":"0c8c2247-c9e9-453a-b609-0535eb6ea468","added_by":"auto","created_at":"2025-07-22 19:25:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":200077,"visible":true,"origin":"","legend":"\u003cp\u003eEastern China Monsoon Region\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/73988f7276f19de051dda7bb.jpeg"},{"id":87331942,"identity":"7cbcb6fc-d35d-49c7-97ed-f45c047557a4","added_by":"auto","created_at":"2025-07-22 19:17:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222787,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution characteristics of heavy rainfall (Figure a shows the annual average frequency of heavy rainfall; Figure b shows the proportion of annual heavy rainfall frequency to total annual rainfall events; Figure c shows the annual average intensity of heavy rainfall)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/a85eb70e14dfc1c6f5718eeb.png"},{"id":87331950,"identity":"3f19994f-95a9-4ac2-a9b2-4f5b0689bf67","added_by":"auto","created_at":"2025-07-22 19:17:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":253716,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the days and intensity’s mean center of heavy rain\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/e7dc7bdaab1a26babe96f3dc.png"},{"id":87331946,"identity":"3deace44-267b-4b3a-b023-89153647df51","added_by":"auto","created_at":"2025-07-22 19:17:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67007,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the average annual change of heavy rainfall in the eastern monsoon region from 1971 to 2020 (Figure a is the average annual frequency of rainstorms; Figure b is the proportion of annual rainstorm frequency to annual rainfall; Figure c is the average annual rainstorm intensity)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/e42f8ce12f0748663536a98e.jpeg"},{"id":87332554,"identity":"23f7c97b-f82c-4512-b8e8-9339b11d162d","added_by":"auto","created_at":"2025-07-22 19:33:28","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82864,"visible":true,"origin":"","legend":"\u003cp\u003eHeavy rainfall risk in the eastern monsoon region of China (Figure a represents the average risk from 2031 to 2050, while Figure b shows the average risk from 2051 to 2080).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/494bec631ee07ed6de550b90.jpeg"},{"id":87331948,"identity":"e858b4c4-231e-4b8a-b0a6-403f73c2ccff","added_by":"auto","created_at":"2025-07-22 19:17:28","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127042,"visible":true,"origin":"","legend":"\u003cp\u003eFuture exposure risk of heavy rainfall in protected areas of the eastern monsoon region of China.(Figure a shows the average exposure from 2031 to 2050, while Figure b displays the average exposure from 2051 to 2080.)\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/689d3feabcd77f0d0676a871.jpeg"},{"id":87332355,"identity":"7787ad0e-53f1-47a3-b806-903dab9e9798","added_by":"auto","created_at":"2025-07-22 19:25:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60723,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of future risks in protected areas (a) National-level protected areas, (b) Provincial-level protected areas, (c) Municipal-level protected areas).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/5ee2b1e30337f24ae6d3836f.png"},{"id":87333041,"identity":"f0d33b1f-77af-486b-af06-f02ce624bf86","added_by":"auto","created_at":"2025-07-22 19:41:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1588712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6888549/v1/a7da8e47-d26f-4b37-93ce-4a26bb18db51.pdf"}],"financialInterests":"","formattedTitle":"Natural Reserves Exposure to Rainstorm in the Eastern Monsoon Region of China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgainst the backdrop of global warming, significantly increased atmospheric water vapor content has accelerated the hydrological cycle, causing heavy rainfall events to exhibit characteristics of increased frequency and intensity (Myhre, Alterskj\u0026aelig;r et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e,Tabari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The IPCC Sixth Assessment Report clearly states that for each 1\u0026deg;C increase in global warming, extreme daily precipitation intensity will increase by approximately 7%. The gradual escalation in rainfall intensity and frequency is threatening biodiversity by altering ecosystem structure and function (Pawson, Brin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) attracting widespread attention from scholars worldwide. Climate change and anthropogenic pressure represent two major global challenges that pose enormous threats to biodiversity (Wan, Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHeavy rainfall poses significant risks to nature reserves through both direct and indirect impacts, affecting comprehensive management, protected wildlife, and their habitats. In the eastern monsoon region of China, heavy rainfall often triggers a range of secondary disasters such as flash floods, landslides, and debris flows. These occur when intense precipitation rapidly saturates the soil, increases surface runoff, and destabilizes slopes, especially in mountainous and hilly areas common to many nature reserves. As a result, nature reserves in this region face significant challenges in comprehensive management. Monitoring instruments are frequently damaged or rendered inoperable due to flooding or power disruptions, undermining the continuity and accuracy of long-term ecological data collection. Infrastructure such as patrol roads is highly vulnerable to washouts and collapses, impeding routine inspections and delaying emergency response efforts\u0026mdash;sometimes isolating entire sections of a reserve. Due to climate change, most species habitats demonstrate trends of reduction or stabilization, with animal and plant habitats shifting from integration toward fragmentation (Yuan, Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Continuous heavy rainfall creates conditions for the outbreak of animal infectious diseases, reduces vegetation quality for herbivores, and even alters herbivores' spatial utilization patterns, including migration (Bartzke, Ogutu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Extreme precipitation causes extensive damage to coastal tree species (Kramer, Vreugdenhil et al., 2008), while disasters such as floods caused by heavy rainfall result in mass mortality of species within wetland ecosystems (Vretare, Weisner et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e,Asaeda and Hung, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Identifying habitats severely impacted by heavy rainfall and implementing protective measures represents a crucial issue that needs to be addressed currently.\u003c/p\u003e\u003cp\u003eChina's eastern monsoon region is a key area for biodiversity conservation. This region not only nurtures rich wildlife resources (Wen, Gu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e),but also faces severe challenges from extreme precipitation events (Shrestha, Xu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a sensitive area to global climate change, China's eastern monsoon region is under dual pressure from monsoon circulation anomalies and regional warming, coupled with topographic uplift effects, making it a hotspot for intense precipitation events (Wang and Zhou, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e,Dong, Noyelle et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Extreme precipitation events have increased significantly in East China, especially in the Yangtze River Basin, with a trend of 10\u0026ndash;20% per decade in summer (Wang and Zhou, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Particularly during the summer monsoon, the frequent convergence of warm and humid airflows from the tropical western Pacific and Indian Oceans with polar cold air, combined with factors such as topographic uplift, leads to frequent regional heavy precipitation events (Dong, Noyelle et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Protected Areas (PA) are globally recognized strategies for addressing the survival crisis of rare animals (Hoekstra, Boucher et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e,Geldmann, Joppa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e,Lewis, MacSharry et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), playing a crucial role in reducing habitat loss and maintaining sustainable population levels of species (Gray, Hill et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To better protect rare flora and fauna, China has established natural reserves to provide habitats for wild animals and plants (Wang, Feng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the current system of protected areas has limitations in responding to precipitation changes and struggles to maintain biodiversity conservation value (Xu and Wu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is necessary to identify those with higher rainfall risks and develop relevant response mechanisms. Therefore, approaching from the perspective of heavy rainfall disasters at a more macro scale to systematically evaluate the exposure risks of natural reserves in China's eastern monsoon region has constructive significance for improving China's natural reserve system.\u003c/p\u003e\u003cp\u003eExisting related studies mostly focus on specific areas within a certain protected area or on individual heavy rainfall events (Wang, Feng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e,Liu, Yuan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with small spatial scales and short time spans, lacking a macro-level portrayal of natural reserves affected by heavy rainfall over large areas and extended time periods. Based on this, our study selects China's eastern monsoon region as the research area. The study defines heavy rainfall events as daily precipitation exceeding 50 mm and systematically analyze the spatiotemporal evolution characteristics of heavy rainfall events in China's eastern monsoon region from 1971 to 2020. The study assesses nature reserves\u0026rsquo; exposure to heavy rainfall disasters and predict exposure risks for 2031\u0026ndash;2050 as the near future and 2081\u0026ndash;2100 as the far future based on multi-model ensemble simulation data from CMIP6. This research not only helps deepen the understanding of regional extreme precipitation event evolution patterns but also provides scientific basis for adaptive conservation management of rare wildlife. By constructing a heavy rainfall risk assessment system on a larger spatiotemporal scale, this study offers reference for improving extreme precipitation prevention and protection measures in China's eastern monsoon region. It provides scientific foundation and reference for future guidance on disaster prevention and mitigation in protected areas and ensuring biodiversity security, with important theoretical and practical significance for enhancing regional biodiversity conservation capacity.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eChina's eastern monsoon region is one of the most typical and unique monsoon regions in the global climate system, spanning a vast geographical area in eastern China with significant climatic, geographical, and ecological characteristics. This region roughly encompasses the eastern Yangtze River, and areas north of the Qinling Mountains-Huaihe River line, including East China, Central China, and parts of North China. It covers part or all of provinces such as Jiangsu, Zhejiang, Shanghai, Anhui, Jiangxi, Hubei, Hunan, Fujian, and Guangdong. The region is densely populated, economically developed, and is also one of the most biodiverse regions in China.The eastern monsoon region is significantly influenced by the East Asian monsoon climate system, exhibiting distinct monsoon climate characteristics. It is one of China's most biodiverse regions, home to numerous national-level natural reserves. These protected areas provide habitats for rare and endangered species such as giant pandas, P\u0026egrave;re David's deer, and white cranes, and contain extremely fragile ecosystems. The complex topography, abundant precipitation, and unique ecological environment of the region have created this treasure trove of biodiversity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data\u003c/h2\u003e\u003cp\u003eWe utilize rainfall data from meteorological stations measured from 1971 to 2020 as historical rainfall observation data, and analyze future rainfall scenarios under SSP2-4.5 from CMIP6 (the Coupled Model Intercomparison Program in Phase 6). We reveal both the spatiotemporal distribution of historical rainfall in the eastern monsoon region and the exposure risks to natural reserves under historical and different future scenarios.\u003c/p\u003e\u003cp\u003eAdditionally, this paper uses point data of ecological functional zones of natural reserves from ArcGIS online China, with a total of 2005 points located in the eastern monsoon region, including 258 national-level, 563 provincial-level, and 258 city/county-level reserves. It should be noted that, due to the high fragmentation of nature reserves in the eastern monsoon region (Zhao, Zong et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), these reserves are characterized by a large number and small size. To better integrate with the gridded data of storms, this study adopted point - based data for the nature reserves.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData Sources\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime Span\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpatial resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTime resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1971\u0026ndash;2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeteorological Station\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina Meteorological Administration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACCESS-CM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACCESS-ESM1-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.125\u0026deg;\u0026times;1.125\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCanESM5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u0026deg; \u0026times; 2.8\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC-EARTH3-VEG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u0026deg; \u0026times; 0.25\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFDL-ESM4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026deg; \u0026times; 1\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\u0026deg; \u0026times; 1.25\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIROC6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4\u0026deg; \u0026times; 1.4\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026deg; \u0026times; 0.5\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9\u0026deg; \u0026times; 1.9\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarth System Grid Federation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Methodology\u003c/h2\u003e\u003cp\u003eThe technical approach of this research is primarily divided into two parts: First, we explore the spatiotemporal variation characteristics of heavy rainfall events in the eastern monsoon region over the 50-year period from 1971 to 2020. Then, based on future precipitation data from individual CMIP6 projection models, we calculate the exposure risk of rare animals to heavy rainfall disasters for both near- and long-term future periods by averaging the results obtained from each model separately. All calculations are implemented in Python and ArcGIS.\u003c/p\u003e\u003cp\u003eSpecifically, this study adopts the definition of extreme rainfall established by the China Meteorological Administration, which classifies a rainfall event as extreme if the daily precipitation exceeds 50 mm. For the multi-model CMIP6 ensemble, all datasets were resampled to match the spatial resolution and extent of the BCC-CSM2-MR model, after which an ensemble mean was calculated to derive the final future projection. For individual CMIP6 models, data from 2015 to 2025 were used as the historical baseline to compute the mean and standard deviation. These parameters were then applied to both near-future and far-future scenarios. Accordingly, the exposure risk under future scenarios assessed in this study represents a relative risk in comparison to the historical baseline.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Exposure Risk Caculation\u003c/h2\u003e\u003cp\u003eThe assessment of exposure risk for rare animals is the core component of this research, and its methodological design must balance scientific validity and systematic approach. This study constructs a multi-dimensional, multi-indicator exposure risk assessment system. The central approach is to eliminate dimensional differences between indicators through standardization and objectively determine the weight of each indicator using information entropy weighting methods. Specifically, we define heavy rainfall intensity as the annual total heavy rainfall and use its standardized value as the intensity indicator; we use the standardized annual heavy rainfall frequency as the frequency indicator; and the average of the standardized Mann-Kendall trend values of both as the trend indicator. After determining the weights for these three indicators, they are summed to obtain the final exposure degree. In the final stage of exposure risk assessment, the calculated heavy rainfall hazard indicators are overlaid with spatial data of natural reserves for rare animals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Rainstorm Center Calculation\u003c/h2\u003e\u003cp\u003eThis research uses Chinese climate zones as grouping fields, dividing the stations in the eastern monsoon region into tropical/subtropical and temperate categories, with geographical location as the foundation. Using the annual average number of heavy rainfall days and annual average heavy rainfall intensity from the 1971\u0026ndash;1980 and 2011\u0026ndash;2020 time periods as weighting fields, we calculate the central locations of average heavy rainfall days and average heavy rainfall intensity over 50 years (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on changes in these central locations, we can observe and analyze the migration characteristics and trends of heavy rainfall centers in different climate regions of the eastern monsoon region.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\u003cp\u003eWhere x\u003csub\u003ei\u003c/sub\u003e and y\u003csub\u003ei\u003c/sub\u003e are the coordinates of feature i, and w\u003csub\u003ei\u003c/sub\u003e is the weight at feature i.\u003c/p\u003e\u003cp\u003eThis research uses Chinese climate zones as grouping fields, dividing the stations in the eastern monsoon region into tropical/subtropical and temperate categories, with geographical location as the foundation. Using the annual average number of heavy rainfall days and annual average heavy rainfall intensity from the 1971\u0026ndash;1980 and 2011\u0026ndash;2020 time periods as weighting fields, we calculate the central locations of average heavy rainfall days and average heavy rainfall intensity over 50 years. Based on changes in these central locations, we can observe and analyze the migration characteristics and trends of heavy rainfall centers in different climate regions of the eastern monsoon region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Other methods\u003c/h2\u003e\u003cp\u003eWe employ the Mann-Kendall test to reveal interannual trends in heavy rainfall. When constructing the exposure risk assessment system, we use the Entropy Weight Method to eliminate the subjectivity of artificial weighting, assigning weights to various factors in the risk assessment system and summing them to obtain a comprehensive risk measurement. The K-Means clustering algorithm is used to classify the risks in the eastern monsoon region into five categories: low risk, relatively low risk, medium risk, relatively high risk, and high risk areas.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003e\u003cb\u003e3.1 Spatiotemporal Distribution Characteristics of Heavy Rainfall in China's Eastern Monsoon Region from 1971 to 2020\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eHeavy rainfall characteristics in China's eastern monsoon region exhibit complex and significantly differentiated spatial distribution patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The regional variation in annual average heavy rainfall days demonstrates notable geographic gradient characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), showing a distinct increasing trend from northwest to southeast. Coastal areas and southern regions have significantly higher numbers of heavy rainfall days than inland and northern regions, a distribution pattern closely related to regional topography, land-sea effects, and monsoon circulation. Specifically, the southernmost regions reach peak values in heavy rainfall days, while the northwestern, northeastern, and southwestern marginal areas have relatively fewer heavy rainfall days.The proportion of heavy rainfall volume, an important indicator measuring the contribution of heavy rainfall to regional precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), also presents significant spatial differences. The heavy rainfall proportion in the northeast region is only 2%-8%, while regions south of the Yangtze River have proportions as high as 20%-30%, highlighting the enormous difference in precipitation characteristics between north and south.The average intensity of heavy rainfall likewise exhibits significant spatial heterogeneity across the eastern monsoon region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Overall, it shows characteristics of greater intensity in the east than in the west and greater in the south than in the north, with intensity decreasing along the northwest to southeast direction. Maximum values are concentrated in the southernmost areas, with central and eastern regions also showing areas of relatively high intensity. Notably, the eastern edge of the Sichuan-Chongqing region in the western part of the study area exhibits abnormally high heavy rainfall intensity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe average centers of heavy rainfall days and intensity were calculated for stations in tropical/subtropical and temperate regions for the two time periods 1971\u0026ndash;1980 and 2011\u0026ndash;2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It can be observed that from the 1970s to the 2010s, the centers of heavy rainfall intensity and days in both tropical/subtropical monsoon regions and temperate monsoon regions show a trend of movement toward the northeast direction. Specifically, the center of heavy rainfall days in the tropical/subtropical monsoon region moved 52.9km in a north-northeast direction (46.6\u0026deg; east of north); the center of heavy rainfall intensity in the tropical/subtropical monsoon region moved 20.6km in a north-northeast direction (47.5\u0026deg; east of north); the center of heavy rainfall days in the temperate monsoon region moved 81.4km in a north-northeast direction (62.6\u0026deg; east of north); and the center of heavy rainfall intensity in the temperate monsoon region moved 71.3km in a north-northeast direction (63.2\u0026deg; east of north). Additionally, it can be seen that the movement angles and distances of the two centers in the temperate zone are more pronounced.It can be observed that the overall migration distance in temperate regions is greater than that in tropical and subtropical regions, indicating that the intensity of strengthening rainfall in the northern parts of temperate regions is greater than in tropical and subtropical regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMigration distances of heavy rainfall centers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistance(km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAngle\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003etropical and subtropical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecenter of days of the rainstorm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.6\u0026deg; north by east\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecenter of intensity of the rainstorm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.5\u0026deg; north by east\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003etemperate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecenter of days of the rainstorm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.6\u0026deg; north by east\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecenter of intensity of the rainstorm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.2\u0026deg; north by east\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe frequency of heavy rainfall events within the eastern monsoon region, the proportion of frequency among total rainfall events, and intensity all exhibit obvious zonal differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In tropical and subtropical regions, the frequency and intensity of heavy rainfall events show a steady upward trend and are significantly higher than in temperate regions. In temperate regions, there is no obvious trend in heavy rainfall frequency and intensity over the 50-year period, but there are large fluctuations around the mean value. The proportion of heavy rainfall frequency to total annual rainfall events in tropical and subtropical regions also shows a clear upward trend, while temperate regions show a smaller upward trend. Notably, around 1996, the two regions converged very closely, while in other years, the tropical and subtropical regions were distinctly higher than the temperate regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Heavy Rainfall Exposure Risk of Natural Reserves in China's Eastern Monsoon Region\u003c/h2\u003e\u003cp\u003eThe spatial distribution of extreme precipitation hazards in China's Eastern Monsoon Region under SSP245 scenario shows distinctive patterns between near and far future periods. In the near-future (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), a clear north-south gradient is observed with lower hazard values predominating the northern portion of the monsoon region while higher hazard levels concentrate in the southeastern coastal areas. The far-future projection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) maintains this fundamental spatial structure but demonstrates significant intensification of hazards, particularly in the southeastern coastal provinces where values approach the maximum of 2.663. Both temporal horizons consistently show a transition zone between low and high precipitation hazards approximately along the mid-latitudes of the Eastern Monsoon Region. The most substantial change between the two periods occurs in the southeastern coastal areas, where moderate hazards in the near future evolve into severe hazards by century's end, indicating an amplification of the monsoon-driven precipitation extremes under continued warming. This pattern highlights the differential vulnerability within the Eastern Monsoon Region, with southern provinces facing increasingly severe precipitation hazards while northern areas experience relatively modest changes in extreme precipitation intensity between the two time periods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial distribution of natural protected areas' exposure to extreme precipitation hazards in China's Eastern Monsoon Region demonstrates clear patterns across different risk levels. In the near-future scenario (Fig,6a), protected areas exhibit a distinctive north-south gradient of exposure, with northern areas predominantly showing minimal to low exposure levels, while southeastern coastal regions display high to extreme exposure levels. The far-future projection (Fig,6b) maintains this spatial pattern but shows an intensification of exposure in the southeastern coastal protected areas, with more sites shifting to extreme exposure categories. Both time periods maintain consistent minimal exposure in northern protected areas, while the transition zone between low and high exposure follows approximately the middle latitudes of the Eastern Monsoon Region. The most notable change between the two periods occurs in the southeastern coastal areas, where moderately exposed protected areas in the near future transition to high or extreme exposure by century's end, indicating increasing vulnerability of conservation areas to precipitation extremes under continued warming in the SSP245 pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis of precipitation exposure risk across different levels of protected natural reserves in China's Eastern Monsoon Region reveals distinct patterns of vulnerability. In national-level reserves (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), there is a declining trend of exposure from low to high risk levels in the near future, with approximately 32% of reserves at risk level 1 and only about 14% at level 5. However, in the far future scenario, the distribution becomes more balanced with notable increases in reserves facing level 2 risk.\u003c/p\u003e\u003cp\u003eProvincial-level reserves (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) show a similar pattern to national reserves in the near future, with higher proportions at lower risk levels, but display a more pronounced shift toward higher risk levels in the far future projection. Notably, about 37% of provincial reserves face level 1 risk in the near future, which decreases in the far future scenario, indicating a transition toward moderate and higher risk categories.\u003c/p\u003e\u003cp\u003eMunicipal-level reserves (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec) exhibit a distinctly different pattern, with a bimodal distribution showing peaks at both the lowest and highest risk categories in both time periods. This suggests municipal reserves are more polarized in their exposure risks compared to national and provincial reserves. Additionally, municipal reserves show the most substantial increase in high-risk exposure from near to far future scenarios.\u003c/p\u003e\u003cp\u003eComparing across administrative levels, national reserves appear to maintain relatively better protection from extreme precipitation risks over time compared to provincial and municipal reserves, which show progressively higher vulnerability to extreme risk levels in future scenarios. This hierarchical pattern suggests that higher administrative-level protected areas may have been established in locations inherently less vulnerable to precipitation extremes, or may have better adaptive management strategies in place.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 The Methodology for Constructing a Rainstorm Exposure Risk Assessment System\u003c/h2\u003e\u003cp\u003eIn the field of risk assessment, understanding the world in relation to risks and how we can and should understand, evaluate, and manage these risks is fundamental. In FEMA's (National Risk Index), hazard exposure is defined as the representative value of buildings (in dollars), population (in numbers and population-equivalent dollars), or agriculture (in dollars) that may be exposed to natural hazard events. Exposure assessments are conducted to identify areas most vulnerable to the effects of heavy rainfall, as well as potential casualties, property damage, and economic losses that may result from heavy rainfall events. Some studies have also selected land use and rainfall intensity as evaluation factors, basing heavy rainfall disaster exposure assessment on both the probability of being affected by heavy rainfall and potential losses (Simin and Shaobo, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study constructs a heavy rainfall exposure risk assessment system primarily based on the frequency of heavy rainfall events and annual heavy rainfall intensity as evaluation factors. After calculating the relevant values of these two evaluation factors for each grid cell within the corresponding research time period, the entropy weight method is used to calculate the weights of these two factors, which are then summed to obtain the hazard distribution. On this basis, the hazard distribution is further overlaid with natural reserves and classified using the k-means clustering method for assessment.Conventional exposure calculation methods typically multiply the disaster frequency within the study period by the carrier area, population, or total GDP value of the region (Tabari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, due to the special nature of the disaster-bearing bodies in this study, we cannot accurately quantify the number of various rare animals within natural reserves. Furthermore, since the area of protected reserves does not have a direct relationship with the disaster situation of rare animals within them, using reserve area directly is not entirely appropriate. Therefore, we adopt a direct overlay approach for statistical analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Consistency Between the Evaluation Results of Medium-High Risk Areas and Actual Conditions\u003c/h2\u003e\u003cp\u003eIn China's eastern monsoon region, the annual average number of heavy rainfall days and intensity generally shows an increasing pattern from northwest to southeast, while the proportion of heavy rainfall is higher in Sichuan and central regions. In the future, China will generally experience an increase in heavy rainfall days, especially in the northeastern regions such as Jilin and Heilongjiang, eastern coastal areas, and parts of Jiangxi and Hunan. From the overall northward migration of heavy rainfall centers, it can be observed that as heavy rainfall increases across the eastern monsoon region, the threat of rainfall growth in northern parts of each region exceeds that in southern parts, consistent with research findings by scholars (LI and ZHAO, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e,HU, DONG et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCombining historical and future heavy rainfall hazards, it can be found that the overall risk for natural reserves in the eastern monsoon region shows a trend of being safer in the northwest direction and more dangerous in the southeast direction, indicating that historical data calculations and CMIP6 prediction results are relatively coherent and comparable. Both historical and future hazard results show that the risk in central China is relatively larger compared to surrounding areas, which is related to the large proportion of heavy rainfall in total precipitation in this region. The results also show that, relatively speaking, historical data is more sensitive in the southernmost coastal areas, while the locations of extreme hazards in future predictions are in central China. This may be due to the large differences in historical heavy rainfall intensity, while the CMIP6 dataset's ability to predict extreme heavy rainfall is insufficient (Dong and Dong, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), weakening the impact of extremely heavy rainfall intensity in coastal areas of Hainan and Guangdong provinces.\u003c/p\u003e\u003cp\u003eIn both the near and distant future, the risk to protected areas in central and southern China remains high, with risks in the near future generally lower than in the distant future. Among all provinces, Guangdong faces the highest current risk for rare animals. Species such as the macaques on Shangchuan Island and the sika deer on Dazhang Island (Zhang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1985\u003c/span\u003e)are under significant threat, which aligns with actual findings (Zheng, Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e,Daily, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e);The risk to rare animals in Guangxi Zhuang Autonomous Region and Hainan Province is somewhat lower, while rare animals in Gansu Province, Shanxi Province, and Inner Mongolia Autonomous Region are relatively safe. In Guangdong, Guangxi, and Hainan, natural reserves need to improve emergency response mechanisms for heavy rainfall, providing facilities such as shelters for rare animals to rest and seek refuge from the rain to ensure their safety. In the future, natural reserves along the Yangtze River in Central China, particularly in Hunan, Hubei, and Jiangxi, as well as the southwestern regions further west (Yang, Gao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e,Wang, Cheng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), will face even greater threats from heavy rainfall. These areas have been severely affected by heavy rainfall disasters in recent years and require enhanced preventive measures.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study finds that heavy rainfall exposure in China\u0026rsquo;s Eastern Monsoon Region shows a clear spatial pattern, increasing from northwest to southeast, with peak intensity in southeastern coastal areas, the western Sichuan region, and the North China Plain. Under the SSP245 scenario, precipitation exposure risk in natural reserves is projected to intensify over time (from 2031\u0026ndash;2050 to 2081\u0026ndash;2100), especially in the southeast, forming a pronounced north-south gradient. Although national reserves generally face lower exposure than provincial and municipal ones, all levels will experience rising risks. These results highlight the need for climate-adaptive conservation planning to protect vulnerable reserves from future extreme rainfall events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis research was funded by Third Xinjiang Scientific Expedition Program (2022xjkk0601), National Natural Science Foundation of China (42471085 and U22B2011), Natural Science Foundation of Hubei Province (2023AFB823).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAvailability Statement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe future rainfall data used in this study are publicly available from the Earth System Grid Federation (ESGF) website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aims2.llnl.gov/search/cmip6/\u003c/span\u003e\u003cspan address=\"https://aims2.llnl.gov/search/cmip6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe historical site-measured rainfall data that support the findings of this study are available from the corresponding author, Sun Shao from the Chinese Academy of Meteorological Sciences, upon reasonable request.\u003c/p\u003e\u003cp\u003eThe nature reserve sites analyzed in this study and detailed data can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mp.weixin.qq.com/s/YtfrODTKUwPFoibTrGFm1Q\u003c/span\u003e\u003cspan address=\"https://mp.weixin.qq.com/s/YtfrODTKUwPFoibTrGFm1Q\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsaeda T, Hung LQ (2007) Internal heterogeneity of ramet and flower densities of Typha angustifolia near the boundary of the stand. Wetlands Ecol Manage 15:155\u0026ndash;164\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBartzke GS, Ogutu JO, Mukhopadhyay S, Mtui D, Dublin HT, Piepho H-P (2018) Rainfall trends and variation in the Maasai Mara ecosystem and their implications for animal population and biodiversity dynamics. PLoS ONE 13(9):e0202814\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaily NM (2020) Jiangmen Taishan Shangchuan Island suffered heavy rainfall and flooded streets, residents said they have long been accustomed to it. from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://static.nfapp.southcn.com/apptpl/videoToShare/index.html?id=3561372#/\u003c/span\u003e\u003cspan address=\"https://static.nfapp.southcn.com/apptpl/videoToShare/index.html?id=3561372#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong C, Noyelle R, Messori G, Gualandi A, Fery L, Yiou P, Vrac M, D\u0026rsquo;andrea F, Camargo SJ, Coppola E (2024) Indo-Pacific regional extremes aggravated by changes in tropical weather patterns. Nat Geosci 17(10):979\u0026ndash;986\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong TY, Dong WJ (2021) Evaluation of extreme precipitation over Asia in CMIP6 models. Clim Dyn 57(7\u0026ndash;8):1751\u0026ndash;1769. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00382-021-05773-1\u003c/span\u003e\u003cspan address=\"10.1007/s00382-021-05773-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeldmann J, Joppa LN, Burgess ND (2014) Mapping Change in Human Pressure Globally on Land and within Protected Areas. Conserv Biol 28(6):1604\u0026ndash;1616\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGray CL, Hill SLL, Newbold T, Hudson LN, Borger L, Contu S, Hoskins AJ, Ferrier S, Purvis A, Scharlemann JPW (2016) Local biodiversity is higher inside than outside terrestrial protected areas worldwide. NATURE COMMUNICATIONS 7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoekstra JM, Boucher TM, Ricketts TH, Roberts C (2005) Confronting a biome crisis: global disparities of habitat loss and protection. Ecol Lett 8(1):23\u0026ndash;29\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHU J, DONG S, Leibin W, LI W and Q. LI (2024) Impact of Human Activities on Seasonal Changes in Precipitation in China. Clim Environ Res 29(6):643\u0026ndash;655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3878/j.issn.1006-9585.2024.23103\u003c/span\u003e\u003cspan address=\"10.3878/j.issn.1006-9585.2024.23103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKramer K, Vreugdenhil SJ (2008) Effects of flooding on the recruitment, damage and mortality of riparian tree species: A field and simulation study on the Rhine floodplain. For Ecol Manag 255(11):3893\u0026ndash;3903Van Der\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewis E, MacSharry B, Juffe-Bignoli D, Harris N, Burrows G, Kingston N, Burgess ND (2019) Dynamics in the global protected-area estate since 2004. Conserv Biol 33(3):570\u0026ndash;579\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLI Y, ZHAO S-S (2022) Floods losses and hazards in China from 2001 to 2020. Clim Change Res 18(2):154\u0026ndash;165. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12006/j.issn.1673-1719.2021.196\u003c/span\u003e\u003cspan address=\"10.12006/j.issn.1673-1719.2021.196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M, Yuan H, Zhou S (2022) Influence of Climate Change on Wolong Nature Reserve. FOREST SCIENCE AND TECHNOLOGY(08. 79\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13456/j.cnki.lykt.2021.08.12.0005\u003c/span\u003e\u003cspan address=\"10.13456/j.cnki.lykt.2021.08.12.0005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMyhre G, Alterskj\u0026aelig;r K, Stjern CW, Hodnebrog \u0026Oslash;, Marelle L, Samset BH, Sillmann J, Schaller N, Fischer E, Schulz M (2019) Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep 9(1):16063\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePawson SM, Brin A, Brockerhoff EG, Lamb D, Payn TW, Paquette A, Parrotta J (2013) Plantation forests, climate change and biodiversity. Biodivers Conserv 22:1203\u0026ndash;1227\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrestha N, Xu X, Meng J, Wang Z (2021) Vulnerabilities of protected lands in the face of climate and human footprint changes. Nat Commun 12(1):1632\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimin Y, Shaobo Z (2015) Exposure Assessment of Rainstorm Disaster Based on Land Use and Precipitation Extreme. A Case Study of Beijing, China.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10(1):13768\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVretare V, Weisner SE, Strand JA, Gran\u0026eacute;li W (2001) Phenotypic plasticity in Phragmites australis as a functional response to water depth. Aquat Bot 69(2\u0026ndash;4):127\u0026ndash;145\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWan X, Jiang G, Yan C, He F, Wen R, Gu J, Li X, Ma J, Stenseth NC, Zhang Z (2019) Historical records reveal the distinctive associations of human disturbance and extreme climate change with local extinction of mammals. Proceedings of the National Academy of Sciences 116(38): 19001\u0026ndash;19008\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang P, Cheng Q, Jin H (2023) Divergent vegetation variation and the response to extreme climate events in the National Nature Reserves in Southwest China, 1961\u0026ndash;2019. Ecol Ind 150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2023.110247\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2023.110247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 110247.DOI\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Feng C, Liu F, Li J (2020) Biodiversity conservation in China: A review of recent studies and practices. Environ Sci Ecotechnology 2:100025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Zhou L (2005) Observed trends in extreme precipitation events in China during 1961\u0026ndash;2001 and the associated changes in large-scale circulation. Geophys Res Lett 32(9)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWen C, Gu L, Wang H, Lv Z, Hu R, Zhong J (2015) GAP analysis on national nature reserves in China based on the distribution of endangered species. Biodivers Sci 23(5):591\u0026ndash;600\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu B, Wu X (2024) A comprehensive analysis to optimizing national-scale protected area systems under climate change. J Environ Manage 363:121408\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang X, Gao D, Wang P (2019) Characteristics of Temperature and Precipitation Changes in Wumeng Mountain Natural Reserve in 57 Years. IOP Conference Series: Earth and Environmental Science 237(2): 022013.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1088/1755-1315/237/2/022013\u003c/span\u003e\u003cspan address=\"10.1088/1755-1315/237/2/022013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYuan R, Zhang N, Zhang Q (2024) The impact of habitat loss and fragmentation on biodiversity in global protected areas. Sci Total Environ 931:173004\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang T (1985) A rare animal breeding experimental base was established on Daman Island. Chin J Wildl 0249DOI. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19711/j.cnki.issn2310-1490.1985.02.023\u003c/span\u003e\u003cspan address=\"10.19711/j.cnki.issn2310-1490.1985.02.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao W, Zong L, Wang M (2024) Spatial distribution of nature reserves in China. Acta Ecol Sin 44(07):2786\u0026ndash;2799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20103/j.stxb.202212103552\u003c/span\u003e\u003cspan address=\"10.20103/j.stxb.202212103552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng W, Huang X, Xu B (2016) Analysis on the characteristics and impact of meteorological disasters in Jiangmen City. Guangdong Meteorol Retrieved 02:38\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Eastern monsoon region, Rainstorm, Spatiotemporal distribution, Natural reserves, Biodiversity, CMIP6","lastPublishedDoi":"10.21203/rs.3.rs-6888549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6888549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of global warming, extreme precipitation events have become increasingly severe. The establishment of natural reserves does not perfectly protect rare animal species from the adverse effects of extreme rainfall. Developing a risk assessment system for heavy rainfall exposure in natural reserves can identify those at heightened risk during precipitation events. This research utilizes observational data from meteorological stations from 1971 to 2020 to reveal spatiotemporal trends of heavy rainfall in the eastern monsoon region of China and establishes an exposure risk assessment framework to evaluate future scenario risks for natural reserves. Results indicate that the annual average number of heavy rainfall days gradually increases from northwest to southeast, displaying a distinct zonal distribution pattern. The proportion of heavy rainfall days to total precipitation days and the average intensity of heavy rainfall show peak centers in the southeastern coastal areas, western Sichuan region, and North China Plain, with minimum values observed in the northwestern direction. Protected areas in China's Eastern Monsoon Region display a north-south gradient of precipitation exposure risk that intensifies from near (2031\u0026ndash;2050) to far (2081\u0026ndash;2100) future under SSP245 scenario, with highest vulnerability in southeastern coastal areas. National reserves generally experience lower exposure than provincial and municipal ones, though all categories face increasing precipitation risks over time.\u003c/p\u003e","manuscriptTitle":"Natural Reserves Exposure to Rainstorm in the Eastern Monsoon Region of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 19:17:23","doi":"10.21203/rs.3.rs-6888549/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-17T15:11:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T14:53:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2025-07-16T08:24:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T14:55:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2025-06-13T09:17:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5965b437-f35d-4337-851c-68000d6f3868","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T19:17:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 19:17:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6888549","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6888549","identity":"rs-6888549","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}