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.

翻译：提议采用统计方法,在分析空间区块最大值数据时选择同质地点,例如在极端事件归因研究中;这些方法以古典假设测试为基础,采用Wald型试验统计,从适当的参数测距仪程序中获得关键值,并纠正其多重性;一个大型蒙特卡洛模拟研究发现,这些方法能够准确识别同质地点,而将选定地点集中起来,可以提高随后统计分析的准确性;该方法通过西欧降水极端值案例研究加以说明;这些方法在R套件中实施,便于在今后极端事件归因研究中应用。