Regional Pooling in Extreme Event Attribution Studies: an Approach Based on Multiple Statistical Testing

TL;DR

This paper introduces statistical methods for selecting homogeneous locations in spatial extreme event data, improving analysis accuracy through pooling, with practical implementation in an R package.

Contribution

It presents a novel approach using hypothesis testing and bootstrap methods for regional pooling in extreme event attribution, enhancing spatial analysis accuracy.

Findings

Methods accurately identify homogeneous locations

Pooling improves statistical analysis accuracy

Approach validated with a case study on European precipitation extremes

Abstract

Statistical methods are proposed to select homogeneous locations when analyzing spatial block maxima data, such as in extreme event attribution studies. The methods are based on classical hypothesis testing using Wald-type test statistics, with critical values obtained from suitable parametric bootstrap procedures and corrected for multiplicity. A large-scale Monte Carlo simulation study finds that the methods are able to accurately identify homogeneous locations, and that pooling the selected locations improves the accuracy of subsequent statistical analyses. The approach is illustrated with a case study on precipitation extremes in Western Europe. The methods are implemented in an R package that allows easy application in future extreme event attribution studies.