Global assessment of historical changes in extreme fire weather: Insight from CMIP6 ensembles and implications for probabilistic attribution to global warming

In response to the occurrence of several large wildfire events across the world in recent years, the question of the extent to which climate change may be altering the meteorological conditions conducive to wildfires has become a hot topic of debate. Despite the development of detection and attribution methodologies for climate change impact assessment in the last decade, studies dedicated explicitly to wildfire, or otherwise extreme 'fire weather', are still relatively few. Here, for the first time, a global probabilistic framework is developed to examine the extent to which externally forced changes in historical global mean surface temperature anomalies (GMSTA) affected the intensity and duration of fire-conducive weather extremes, defined by the Fire Weather Index (FWI). We use six climate model large ensembles (>10 ensemble members) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), to extract the forced response of GMSTA. After evaluating the performances of these climate models in simulating fire weather extremes, we examine changes in the probability of fire weather extremes using extreme value distributions, fitted with annual maxima in both FWI intensity and duration, and scaled to externally forced GMSTA. Global probability ratio maps are used to quantify the influence of rising global temperatures on the changing frequency and duration of FWI extremes, and highlight the sensitivity of estimates of historical changes in extreme fire weather to the climate model ensemble chosen for the analysis. A multi-model synthesis accounting for performance of each model confirms an increasing trend in the probability and duration of extreme fire weather linked to externally forced changes in GMSTA, with the largest increases found in southern North America, south-eastern Europe and parts of Australia. The results of the selective synthesis differ from those obtained via a conventional multi-model averaging that does not account for model performance, thereby demonstrating the value added by model evaluation and selection in maximising the robustness of probabilistic attribution studies.