When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel methodology that combines spatial extreme-value theory with a machine learning (ML) algorithm to model weather extremes and quantify probabilities associated with the occurrence, intensity and spatial extent of these events. The model is here applied to Western European summertime heat extremes. Using new loss functions adapted to extreme values, we fit a theoretically-motivated spatial model to extreme positive temperature anomaly fields from 1959-2022, using the daily 500-hpa geopotential height fields across the Euro-Atlantic region and the local soil moisture as predictors. Our generative model reveals the importance of individual circulation features in determining different facets of heat extremes, thereby enriching our process understanding of them from a data-driven perspective. The occurrence, intensity, and spatial extent of heat extremes are sensitive to the relative position of individual ridges and troughs that are part of a large-scale wave pattern. Heat extremes in Europe are thus the result of a complex interplay between local and remote physical processes. Our approach is able to extrapolate beyond the range of the data to make risk-related probabilistic statements, and applies more generally to other weather extremes. It also offers an attractive alternative to physical model-based techniques, or to ML approaches that optimise scores focusing on predicting well the bulk instead of the tail of the data distribution.

翻译：摘要：当极端天气事件影响大面积区域时，其区域至次大陆尺度的空间范围对灾害影响至关重要。我们提出了一种新颖的方法论，将空间极值理论与机器学习算法相结合，用于模拟极端天气事件，并量化这些事件的发生概率、强度及其空间范围。该模型应用于西欧夏季高温极端事件。通过采用针对极值设计的新损失函数，我们利用1959-2022年每日欧亚大陆地区500百帕位势高度场和局部土壤湿度作为预测变量，将一个基于理论的空间模型拟合至极端正温度异常场。我们的生成模型揭示了不同环流特征在决定高温极端事件多个维度中的重要性，从而从数据驱动视角深化了对这些事件过程机制的理解。高温极端事件的发生、强度及空间范围对构成大尺度波动模式的个别脊槽相对位置敏感。因此，欧洲高温极端事件是局地与远程物理过程复杂相互作用的结果。本方法能够超越数据范围进行外推，生成与风险相关的概率性结论，并可广泛适用于其他极端天气事件。此外，它还为基于物理模型的技术或仅优化整体（而非尾部）数据分布预测性能的机器学习方法提供了有竞争力的替代方案。