The occurrence of several precipitation extremes over sub-seasonal time windows can have major impacts on human societies, leading for instance to floods. Here, we apply a simple statistical framework based on Ripley’s K function, at a global scale and for each season separately, to identify regions where precipitation extremes tend to cluster in time over timescales of a few days to a few weeks. We analyze several observational and reanalysis datasets, as well as output from CMIP6 Global Climate Models (GCMs). Good agreement is found on the spatio-temporal clustering patterns across datasets. Sub-seasonal temporal clustering is largely concentrated over the tropical oceans, where it can be detected year-round. It is also significant over certain tropical lands, like Eastern Africa, and seasonally outside the tropics in several regions, most notably around the eastern subtropical oceans (Iberian Peninsula and Western North America during the DJF and MAM seasons) Southwest Asia (especially during JJA and SON) and Australia (in SON). We also find that CMIP6 models generally correctly reproduce clustering patterns, paving the way for an assessment of trends in sub-seasonal clustering under climate change. Clustering of present-day extremes increases in many areas under climate change. Changes diagnosed by comparing present day and future extreme percentiles are positive and negative and strongest in the tropical areas.

在次季节时间窗口内发生的几个极端降水事件会对人类社会产生重大影响，例如导致洪水。在这里，我们应用基于 Ripley's K 函数的简单统计框架，分别在全球范围内和每个季节，以识别极端降水往往在几天到几周的时间尺度内随时间聚集的区域。我们分析了几个观测和再分析数据集，以及来自 CMIP6 全球气候模型 (GCM) 的输出。在跨数据集的时空聚类模式上发现了良好的一致性。次季节性时间聚类主要集中在热带海洋上，全年都可以检测到。它在某些热带土地上也很重要，例如东非，以及在几个地区的季节性热带以外，最显着的是东亚热带海洋（DJF 和 MAM 季节期间的伊比利亚半岛和北美西部）、西南亚（特别是在 JJA 和 SON 期间）和澳大利亚（在 SON 期间）。我们还发现 CMIP6 模型通常正确地再现了聚类模式，为评估气候变化下的亚季节聚类趋势铺平了道路。在气候变化下的许多地区，当今极端事件的聚集增加。通过比较当前和未来的极端百分位数而诊断出的变化在热带地区是积极的和消极的，并且最强。为评估气候变化下的次季节聚集趋势铺平道路。在气候变化下的许多地区，当今极端事件的聚集增加。通过比较当前和未来的极端百分位数而诊断出的变化在热带地区是积极的和消极的，并且最强。为评估气候变化下的次季节聚集趋势铺平道路。在气候变化下的许多地区，当今极端事件的聚集增加。通过比较当前和未来的极端百分位数而诊断出的变化在热带地区是积极的和消极的，并且最强。