The impact of an extreme climate event depends strongly on its geographical scale. Max-stable processes can be used for the statistical investigation of climate extremes and their spatial dependencies on a continuous area. Most existing parametric models of max-stable processes assume spatial stationarity and are therefore not suitable for the application to data that cover a large and heterogeneous area. For this reason, it has recently been proposed to use a clustering algorithm to divide the area of investigation into smaller regions and to fit parametric max-stable processes to the data within those regions. We investigate this clustering algorithm further and point out that there are cases in which it results in regions on which spatial stationarity is not a reasonable assumption. We propose an alternative clustering algorithm and demonstrate in a simulation study that it can lead to improved results.

翻译：极端气候事件的影响在很大程度上取决于其地理尺度。最大稳定过程可用于统计研究气候极值及其在连续区域上的空间依赖性。现有的大多数最大稳定过程参数模型都假设空间平稳性，因此不适用于覆盖范围大且异质性强的区域数据分析。为此，近期有研究提出使用聚类算法将研究区域划分为若干子区域，并在这些子区域内拟合参数化最大稳定过程模型。本文对该聚类算法进行了深入探讨，发现某些情况下该算法划分的区域仍难以满足空间平稳性假设。我们提出了一种替代性的聚类算法，并通过模拟实验证明该算法能够取得更优的结果。