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Abstract The task of simplifying the complex spatio-temporal variables associated with climate modelling is of utmost importance and comes with significant challenges. In this research, our primary objective is to develop a clustering framework to handle compound extreme events within gridded climate data across Europe. Specifically, we intend to identify subregions that display asymptotic independence between precipitation and wind speed extremes, meaning that occurrences of extreme rain and wind speed in one subregion do not affect those in the other. To achieve this, we utilize 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. 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.
Boulin et al. (Tue,) studied this question.