  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.
