AUTHOR=Xu Wanling , Zhang Meng , Hu Zengyun , Guan Xiaojun , Jiang Lizhi , Bao Ruijuan , Wei Yingying , Ma Miaomiao , Wei Jianhui , Gao Lu TITLE=Spatial and temporal heterogeneity of tropical cyclone precipitation over China from 1959 to 2018 JOURNAL=Frontiers in Environmental Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.984395 DOI=10.3389/fenvs.2022.984395 ISSN=2296-665X ABSTRACT=

Tropical cyclone precipitation (TCP) can cause serious floods and urban waterlogs as well as cause various secondary disasters, such as landslides and debris flows, which negatively affect human lives and the sustainable development of the economy. This study applied the prewhitening Mann-Kendall test, empirical orthogonal function, and continuous wavelet transform to investigate the long-term trend, spatiotemporal pattern, and periodicity of TCP at monthly, interannual, and interdecadal timescales over China. The recurrence risks of extreme TCP were analyzed using the return period estimation model. The results showed that 1) TCP displayed a significant increasing trend, especially in eastern China, inland areas, and Guangxi Province. The TCP periodicities were 2.5 and 4.9 years across all of China. However, TCP cycles had large discrepancies in the time and frequency domains in different subregions. 2) Monthly TCP demonstrated a decreasing trend in May and an increasing trend from June to October in all of China. The TCP in northeastern China and southern China tended to decrease in July and August, respectively. 3) TCP demonstrated a decreasing tendency from the 1960s–1980s followed by a rebounding trend in the 1990s–2010s. In addition, TCP showed a dipole mode in the 1970s and 2000s. 4) There was an increasing recurrence risk of extreme TCP in the Yangtze River Delta, Hainan Province, southeastern Guangxi Province, and southwestern Guangdong Province. It is therefore necessary to improve forecasting of extreme TCP events to improve risk management and prevention capacity of natural disasters, especially in regions with high population and economy exposure.