A model weighting scheme for fire weather projections simulated by CMIP6 climate model ensembles

Weather and climate play an important role in shaping global fire regimes and geographical distributions of burnable areas. At the global scale, fire danger is likely to increase in the near future due to warmer temperatures and changes in precipitation patterns, as projected by the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). There is a need to develop the most reliable projections of future climate-driven fire danger to enable decision makers and forest managers to respond to future fire events.

Climate change projections generated by general circulation models, especially those that contribute to the 6^th Coupled Model Intercomparison Project (CMIP6), are the most important basis in understanding future changes in fire-conducive weather and climate associated with a warming world. However, errors and biases inherent to such models are rarely taken into account when generating climate change projections. For fire weather in particular, projections have typically been expressed by a single model or through a multi-model mean. This approach can be misleading, as it explains little about the consensus among different models and their uncertainties. Here, following a comprehensive evaluation of the performance of 16 different CMIP6 climate model ensembles, we present new scenarios for detecting changes in fire-prone conditions based on a statistical weighting approach that accounts for both model skill and independence. We demonstrate the value of a weighted approach in accounting for and reducing model uncertainties, and more generally in the development of fire weather scenarios that ultimately as useful as possible. In conclusion, we make recommendations for how the new set of scenarios can benefit end users in decision-making and forest management.