When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity, and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals the importance of individual circulation features in determining different facets of heat extremes, thereby enriching our process understanding from a data-driven perspective. Heat extremes are sensitive to the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.

翻译：当极端天气事件影响广大区域时，其区域至次大陆尺度的空间范围对其影响至关重要。我们提出了一种新颖的机器学习（ML）框架，该框架整合了空间极值理论来建模天气极端事件，并量化与这些事件的发生、强度和空间范围相关的概率。我们的方法采用了适用于极值的新损失函数，使模型能够优先关注数据分布的尾部而非主体部分。通过应用于西欧夏季高温极端事件的案例研究，我们使用每日500百帕位势高度场和局地土壤湿度作为预测因子，以捕捉局地与远程物理过程之间复杂的相互作用。我们的生成模型揭示了单个环流特征在决定高温极端事件不同方面的重要性，从而从数据驱动的视角丰富了我们对过程的理解。高温极端事件对作为大尺度波型一部分的高空脊和槽的相对位置十分敏感。我们的方法能够外推至数据范围之外，以做出与风险相关的概率性陈述。该方法更广泛地适用于其他天气极端事件，并为传统物理方法和基于机器学习的技术提供了一种替代方案，后者较少关注天气数据的极端特性。