Benchmarking performance of annual burn probability modeling against subsequent wildfire activity in California

Wildfire simulation is deployed extensively to support risk management, and in the US has driven billions in federal investment. Foundational to strategic risk analysis is spatial information on the likelihood of burning in a fire year, typically provided by burn probability (BP) models. The recency of BP maps is a key driver of their accuracy, especially in disturbed landscapes that have experienced changes in fire spread potential. Few published examples exist comparing BP values against subsequent fire activity, and none to our knowledge evaluate annually updated BP maps. Here, we present a novel performance evaluation of the operational wildfire simulation system FSim, confronting updated BP maps with subsequent fire activity across the state of California over a 4-year period (2020–2023). Results show strong predictive ability: across 5 equal-area BP classes, 56.7–79.8% of the burned area occurred in the top 20% of mapped area; mean (median) BP values in burned areas were 238.5-348.8% (551.4-880.7%) greater than in unburned areas; empirical cumulative distribution functions of BP for burned/unburned areas were statistically significant; mean Log Skill Scores ranged from 0.276–0.339 against two reference models. Findings indicate reliable forecast performance and useful application of up-to-date BP maps, critical to support ongoing wildfire risk mitigation.