Non-stationary time series attribution for heatwaves over Europe

The increasing occurrence of extreme weather events since the beginning of the 21st century has led to the development of new methods to attribute extreme events to anthropogenic climate change. How the extreme event is defined has a major influence on the attribution result. A frequently disregarded or evaded aspect concerns the temporal dependence and the clustering of extremes.  This study presents an approach for attributing complete time series during extreme events to anthropogenic forcing, which eliminates the need for declustering. The approach is based on a non-stationary Markov process using bivariate extreme value theory to model the temporal dependence of the time series. We calculate the likelihood ratio of an observational time series from ERA5 given the distributions as estimated from CMIP6 simulations with historical natural-only and natural and anthropogenic forcing scenarios. The spatial fields are condensed by the extremal pattern index as a compact description of spatial extremes. In addition, the study examines the extent to which attribution statements about the occurrence of extreme heat events change when the effect of the mean warming is eliminated.

The resulting attribution statement provides very strong evidence for the scenario with anthropogenic drivers over Europe, especially since the beginning of the 21st century. For central and southern Europe, the influence of anthropogenic greenhouse gas emissions on heatwaves could already have been proven in the 1970s using today's methods. Apart from a general rise in temperature, no other reliable signals could be detected, neither with regard to the temporal dependence of days with extreme heat nor with regard to the shape of the extreme value distribution.