The task of simplifying the complex spatio-temporal variables associated with climate modeling is of utmost importance and comes with significant challenges. In this research, our primary objective is to tailor clustering techniques to handle compound extreme events within gridded climate data across Europe. Specifically, we intend to identify subregions that display asymptotic independence concerning compound precipitation and wind speed extremes. To achieve this, we utilise daily precipitation sums and daily maximum wind speed data derived from the ERA5 reanalysis dataset spanning from 1979 to 2022. Our approach hinges on a tuning parameter and the application of a divergence measure to spotlight disparities in extremal dependence structures without relying on specific parametric assumptions. We propose a data-driven approach to determine the tuning parameter. This enables us to generate clusters that are spatially concentrated, which can provide more insightful information about the regional distribution of compound precipitation and wind speed extremes. In the process, we aim to elucidate the respective roles of extreme precipitation and wind speed in the resulting clusters. The proposed method is able to extract valuable information about extreme compound events while also significantly reducing the size of the dataset within reasonable computational timeframes.

翻译：简化与气候建模相关的复杂时空变量任务至关重要，但面临重大挑战。本研究旨在定制聚类技术，以处理欧洲网格化气候数据中的复合极端事件，具体目标是识别在复合降水和风速极端事件中呈现渐近独立性的子区域。为此，我们利用ERA5再分析数据集（1979-2022年）的日降水量总和与日最大风速数据。方法基于一个调优参数和散度度量，无需特定参数假设即可突出极端依赖结构的差异。我们提出数据驱动方法确定调优参数，从而生成空间集中分布的聚类，为揭示复合降水和风速极端事件区域分布提供更具洞察力的信息。在分析过程中，我们旨在阐明极端降水和风速在最终聚类结果中的各自作用。该方法能够在合理计算时间内提取关于极端复合事件的有价值信息，同时显著缩减数据集规模。