Record events attribution in climate studies

Within the statistical climatology literature, inferring the contributions of potential causes with regard to climate change has become a recurrent research theme during this last decade. In particular, disentangling human induced (anthropogenic) forcings from natural causes represents a nontrivial statistical task, especially when the focal point moves away from mean behaviors and goes towards extreme events with high societal impacts. Most studies found in the field of extreme event attributions (EEA) rely on extreme value theory. Under this theoretical umbrella, it is often assumed that, for a given location, temporal changes in extremes can be detected in both location and scale parameters of an extreme value distribution, while its shape parameter remains unchanged over time. This assumption of constant tail shape parameters between a so-called factual world (all forcings) and a counterfactual one (without anthropogenic forcing) can be challenged due to the fact that important forcing changes could impact large scale atmospheric and oceanic circulation patterns, and consequently, the latter can reshape the full distribution, including its shape parameter that drives extremal behavior. In this article, we study how allowing different tail shape parameters between the factual and counterfactual worlds can affect the analysis of records. In particular, we extend the work of Naveau et al. in which this case was not treated. We also add properties and theoretical inferential results about records in EEA and propose a procedure for model validation. A simulation study of our approach is detailed. Our method is applied to records of yearly maxima of daily maxima of near-surface air temperature issued from the numerical climate model CNRM-CM6-1 of Météo-France.