标题： 在气候极端事件归因中尾部假设的重要性 显示英文标题

标题： On the importance of tail assumptions in climate extreme event attribution

摘要： 极端天气事件正变得越来越频繁和强烈，对人类生命、生物多样性和生态系统构成严重威胁。 极端事件归因（EEA）的一个关键目标是评估人为气候变化在多大程度上影响这些事件。 EEA的核心是对大气极端事件进行准确的统计表征，由于它们是在高维网格上测量的，因此本质上是多变量或空间的。 在Pearl的反事实因果推理框架内，我们通过比较三种多变量建模方法来估计因果度量，评估尾部假设如何影响归因结论。 这些方法包括：(i) 多变量广义帕累托分布，它施加了一个不变的尾部依赖结构；(ii) Castro-Camilo和Huser（2020）的因子Copula模型，它提供了灵活的次渐近行为；以及(iii) Huser和Wadsworth（2019）的模型，它在不同形式的极值依赖之间平滑过渡。 我们在模拟场景（在不同形式的模型误设下）和实际数据应用中评估了这些建模选择的影响，使用了Météo-France CNRM模型在欧洲的每周冬季最大值和ACCESS-CM2模型在美国的日降水量。我们的研究结果表明，尾部假设在EEA中显著影响因果度量。 极值依赖结构的误设可能导致明显不同且可能具有误导性的归因结论，这强调了在量化气候变化对极端事件的影响时，需要仔细选择和评估模型。 显示英文摘要

摘要： Extreme weather events are becoming more frequent and intense, posing serious threats to human life, biodiversity, and ecosystems. A key objective of extreme event attribution (EEA) is to assess whether and to what extent anthropogenic climate change influences such events. Central to EEA is the accurate statistical characterization of atmospheric extremes, which are inherently multivariate or spatial due to their measurement over high-dimensional grids. Within the counterfactual causal inference framework of Pearl, we evaluate how tail assumptions affect attribution conclusions by comparing three multivariate modeling approaches for estimating causation metrics. These include: (i) the multivariate generalized Pareto distribution, which imposes an invariant tail dependence structure; (ii) the factor copula model of Castro-Camilo and Huser (2020), which offers flexible subasymptotic behavior; and (iii) the model of Huser and Wadsworth (2019), which smoothly transitions between different forms of extremal dependence. We assess the implications of these modeling choices in both simulated scenarios (under varying forms of model misspecification) and real data applications, using weekly winter maxima over Europe from the M\'et\'eo-France CNRM model and daily precipitation from the ACCESS-CM2 model over the U.S. Our findings highlight that tail assumptions critically shape causality metrics in EEA. Misspecification of the extremal dependence structure can lead to substantially different and potentially misleading attribution conclusions, underscoring the need for careful model selection and evaluation when quantifying the influence of climate change on extreme events.