Abstract

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 modelling 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 sub-asymptotic behaviour; and (iii) the model of Huser and Wadsworth [2019], which smoothly transitions between different forms of extremal dependence. We assess the implications of these modelling choices in both simulated scenarios—under varying forms of model misspecification—and real data applications, using weekly winter maxima from the Météo-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. Misspecifying the extremal dependence structure can lead to substantially different—and potentially misleading—attribution conclusions, underscoring the need for careful model selection when quantifying the influence of climate change on extreme events.