期刊名称：Advances in Statistical Climatology, Meteorology and Oceanography
印刷版ISSN：2364-3579
电子版ISSN：2364-3587
出版年度：2020
卷号：6
期号：2
页码：205-221
DOI：10.5194/ascmo-6-205-2020
出版社：Copernicus Publications
摘要：We develop an extension of the statistical approach by Ribes et al. (2020), which was designed for Gaussian variables, for generalized extreme value (GEV) distributions. We fit nonstationary GEV distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP) models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. The new method is applied to the July 2019 French heat wave. Our results show that both the probability and the intensity of that event have increased significantly in response to human influence. Remarkably, we find that the heat wave considered might not have been possible without climate change. Our results also suggest that combining model data with observations can improve the description of hot temperature distribution.
