Explaining building damage from wildfires in California.

A physically interpretable, data-driven framework was developed to elucidate causal interactions, model, and predict wildfire-induced building damage across California. More than 100,000 damage inspection records (2013 to 2024) were used to model building damage from static environmental variables (topography, vegetation, and human footprint), dynamic weather inputs, and a proposed Composite Building Flammability Rating (CBFR). Three model configurations were tested: (i) a comprehensive model integrating all variables, (ii) an enviro-weather hybrid excluding CBFR, and (iii) an environmental exposure model excluding both weather and CBFR. A strict 200-meter spatial dead-zone constraint was applied to eliminate local autocorrelation, and the comprehensive model achieved 88% (±0.4%) accuracy, which dropped to 82.9% (±0.6%) without CBFR and to 74.5% (±0.5%) without both weather and CBFR. Spatial grid-based cross-validation demonstrated a diverse accuracy of 68.0 (±17%), 66.0 (±16%), and 62.0 (±13%), respectively. Building flammability, dew point temperature, and near-surface wind speed were identified as the most important predictors of damage. Vapor pressure deficit had the strongest causal effect on damage probability, though spatial variability was observed in the causal effects of climate and geographic variables. A 100-meter-resolution Wildfire Building Damage Risk Index was also developed to highlight high-risk damage zones. Findings emphasize that wildfire impacts in the wildland-urban interface result from a confluence of structural vulnerability, atmospheric dryness, and fuel exposure, offering scalable tools for risk forecasting, defensible space planning, and climate-resilient infrastructure development.