江苏沿海地区极端降水的统计特征分析
Analysis of Statistical Features for Extreme Rainfall in Coastal Areas of Jiangsu

全球变暖气候背景下极端降水事件频发，为研究江苏沿海地区极端降水统计特征，基于江苏沿海地区1961~2022年21个国家站逐日降水数据及2013~2022年21个国家站和539个区域站逐小时雨量数据筛选出符合阈值标准的雨量站次。本文选取6个极端降水指数通过MK突变检验和小波分析提取出了江苏沿海地区63a来降水变化趋势并对三个市做了分析。结果表明：(1) 所有指标在30~40a尺度下有20a左右的周期性，在20a的尺度下有10a的周期性均并且以“强弱强”分布。(2) 大部分指标集中在2013、2014年发生突变，其中SDII和R99P有显著增长趋势。(3) 对于区域极端指标，PRCPTOT、R95P、R99P呈南高北低分布，而RX1DAY、RX5DAY以及SDII呈南低北高分布。(4) 在沿海三市中，南通市的极端降水指标均增长趋势显著，而三个市所有指标的周期性与沿海地区大致相同。
Extreme rainfall events occur frequently under the background of global warming. In order to study the statistical characteristics of extreme rainfall in the coastal areas of Jiangsu, based on the daily dataset of 21 national stations in the coastal areas of Jiangsu from 1961 to 2022 and 21 national stations and 539 regions in the coastal areas of Jiangsu from 2013 to 2022, hourly rainfall data recorded by stations is used to filter out rainfall that meet the threshold criteria. This paper selects 6 extreme indices and extracts the 63-year precipitation change trend and periodicity in the coastal areas of Jiangsu and its three cities through MK test and wavelet analysis. The results show that: (1) All indices have a periodicity of about 20 years on a 30~40 years scale, and a periodicity of 10 years on a 20 year scale, and are distributed in a “strong, weak, strong” manner. (2) Most indicators mutated in 2013 and 2014, among which SDII and R99P showed significant growth trends. (3) For regional extreme indicators, PRCPTOT, R95P, and R99P are distributed higher in the south and lower in the north, while RX1DAY, RX5DAY, and SDII are distributed lower in the south and higher in the north. (4) Among the three coastal cities, the extreme precipitation indices in Nantong all have a significant growth trend, and the periodicity of all indices in these cities is roughly the same as that of the coastal area of Jiangsu.