A hierarchical max-stable spatial model for extreme precipitation

TL;DR

This paper introduces a new hierarchical max-stable spatial model for extreme precipitation that addresses the limitations of existing models by enabling Bayesian analysis and capturing spatial dependence effectively.

Contribution

A novel random effects model that produces max-stable processes with a Gaussian extreme value process as a limit, improving modeling of spatial extremes.

Findings

Model successfully analyzes regional climate data

Captures spatial dependence in extreme precipitation

Enables Bayesian inference for max-stable processes

Abstract

Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence. We show that our specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case. The proposed model is used to analyze the yearly maximum precipitation from a regional climate model.