Non-stationary max-stable models with an application to heavy rainfall data

Abstract In recent years, parametric models for max-stable processes have become a popular choice for modeling spatial extremes because they arise as the asymptotic limit of rescaled maxima of independent and identically distributed random processes. Apart from a few exceptions for the class of extremal- t processes, existing literature mainly focuses on models with stationary dependence structures. In this paper, we propose a novel non-stationary approach that can be used for both Brown–Resnick and extremal- t processes – two of the most popular classes of max-stable processes – by including covariates in the corresponding variogram and correlation functions, respectively. While max-stable processes with deterministic covariates inherit most of the properties from classical max-stable processes, we additionally investigate theoretical properties of max-stable processes conditional on random covariates. We show that these can result in both asymptotically dependent and asymptotically independent processes. Thus, conditional models are more flexible than classical max-stable models. In numerical experiments, we study the finite-sample performance of pairwise likelihood estimators for the novel non-stationary models in both scenarios. Furthermore, we apply our approach to extreme precipitation data in two regions in Southern and Northern Germany and compare the results to existing stationary models in terms of Takeuchi’s information criterion (TIC). Our results indicate that, for this case study, non-stationary models are more appropriate than stationary ones for the region in Southern Germany.