  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.
