Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany

Abstract. We use sophisticated machine-learning techniques on a network of summer temperature and precipitation time series taken from stations throughout Germany for the years from 1960 to 2018. In particular, we consider (normalized) maximized mutual information as the measure of similarity and expand on recent clustering methods for climate modeling by applying a weighted kernel-based k-means algorithm. We find robust regional clusters that are both time invariant and shared by networks defined separately by precipitation and temperature time series. Finally, we use the resulting clusters to create a nonstationary model of regional summer temperature extremes throughout Germany and are thereby able to quantify the increase in the probability of observing high extreme summer temperature values (>35 ∘C) compared with the last 30 years.