Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales

Weather and climate play an important role in shaping globalwildfire regimes and geographical distributions of burnable area. Asprojected by the Sixth Assessment Report of the Intergovernmental Panel onClimate Change (IPCC-AR6), in the near future, fire danger is likely toincrease in many regions due to warmer temperatures and drier conditions.General circulation models (GCMs) are an important resource in understandinghow fire danger will evolve in a changing climate, but, to date, thedevelopment of fire risk scenarios has not fully accounted for systematicGCM errors and biases. This study presents a comprehensive global evaluationof the spatiotemporal representation of fire weather indicators from theCanadian Forest Fire Weather Index System simulated by 16 GCMs from thesixth Coupled Model Intercomparison Project (CMIP6). While at the globalscale, the ensemble mean is able to represent variability, magnitude andspatial extent of different fire weather indicators reasonably well whencompared to the latest global fire reanalysis, there is considerableregional and seasonal dependence in the performance of each GCM. To supportthe GCM selection and application for impact studies, the evaluation resultsare combined to generate global and regional rankings of individual GCMperformance. The findings highlight the value of GCM evaluation andselection in developing more reliable projections of future climate-drivenfire danger, thereby enabling decision makers and forest managers to taketargeted action and respond to future fire events.

Automated summary

Weather and climate have a significant impact on global wildfire patterns and the distribution of burnable areas. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6), fire danger is expected to increase in many regions due to warmer temperatures and drier conditions in the near future. General circulation models (GCMs) are important tools for understanding the evolution of fire danger under a changing climate. However, current fire risk scenarios have not fully considered the errors and biases in GCMs. This study evaluates the representation of fire weather indicators simulated by 16 GCMs and emphasizes the importance of GCM evaluation and selection for developing reliable projections of future climate-driven fire danger.