Concurrent regional precipitation can cause water disasters and have an enormous impact on the environment and society. In the present study, regional precipitation events (RPEs) were computed using daily precipitation records from 1989 to 2018 recorded at 173 meteorological stations in the Beijing–Tianjin–Hebei (Jing‐Jin‐Ji) region. This region is famous for its political and economic status and suffers from numerous water disasters. When more than 5% of stations record over 20 mm precipitation on a specific day, it is termed as an RPE. This study describes the duration, intensity, and coverage area of RPEs in the Jing‐Jin‐Ji region. Moreover, it analyses the region's long‐term spatiotemporal evolution of RPEs to explore more complete precipitation characteristics over the study region. The results reveal that the frequency of RPEs decreased with longer duration, and most RPEs with a rainfall intensity of approximately 10 mm⋅day−1 or covered more than 90% of the stations. The coverage area was positively correlated with duration and intensity. RPEs mainly occurred from June to August, with the average ratio of RPEs' total wet day precipitation (PRCPTOT) to regional PRCPTOT being 0.72. The contribution of RPEs to regional annual precipitation increased significantly after 1997. During the past 30 years, the number of RPEs initially decreased and then increased significantly. During strong El Niño decaying summers, the frequency of RPEs was much higher. The spatial distribution of RPEs was affected by topography, land, and sea locations, with greater frequency in the coastal plains. Approximately 28.8% of the RPEs were accompanied by housing losses, which were highly prone to catastrophe compared to extreme precipitation at individual stations. These findings have the potential to improve evaluations of the impacts of water disasters.
The characteristics of extreme heat events (EHE) are changing under global warming. A comprehensive assessment of extreme heat hazards is important for a better understanding of extreme heat distribution and agricultural mitigation planning. In the present study, an extreme heat hazard assessment model is defined by its probability and severity. Continuous days and accumulative temperatures are used to describe the severity of EHE. Daily maximum temperature data from 26 stations in the Haihe Plain during 1960–2019 are used to identify EHE during the summer maize growing seasons. This region is the main summer maize planting area and suffers from numerous EHE. Copula functions are used to estimate the joint probability distributions of continuous days and the accumulative temperature of EHE. The fitted probabilities are then used in the hazard assessment model. The results reveal that the most severe extreme heat hazard areas are found in the western Haihe Plain, the frequency, average continuous days and accumulative temperature of EHE are the largest in this area. The extreme heat hazards are decreased to the northeastern coast area. Annual variations in extreme heat hazard show two change points near 1968 and 1996 that divide the research period into three main stages: higher, lower, and higher extreme heat hazard stage. These findings provide a comprehensive view of extreme heat over the Haihe Plain, which can be used for better extreme heat management and agriculture planning.
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