How to combine climate models and observations in event attribution?

We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (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. This new method is applied to the July 2019 French heatwave. 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.