The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics

This archive contains the data and code used to produce the Western United States Large Forest-Fire Stochastic Simulator (WULFFSS), version 1.0, which is a monthly gridded forest-fire model using interpretable statistics. The WULFFSS operates at 12-km resolution and calculates monthly probabilities of forest fires ≥100 ha as well as the area burned per fire. The model is forced by variables related to vegetation, topographic, anthropogenic, and climate factors, organized into three indices representing spatial, annual-cycle, and lower frequency temporal domains. These indices can interact, so variables promoting fire in one domain amplify fire-promoting effects in another. The fire probability and size modules use multiple logistic and linear regression, respectively, and can be easily updated as new data or ideas emerge. During its training period of 1985–2024, WULFFSS captures >70% and >80% of observed interannual variability in western US forest-fire frequency and area, respect..., , # The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics

Dataset DOI: [10.5061/dryad.63xsj3vdb](10.5061/dryad.63xsj3vdb)

## Description of the data and file structure

This repository contains the data and code used to produce version 1.0 of the Western United States Large Forest-Fire Stochastic Simulator (WULFFSS), as well as the equations that comprise the model and code to run the model. The WULFFSS simulates the probabilities and sizes of forest fires at least 1 km2 in size every month across forested areas of the western US on a 12-km resolution grid. The model is forced by variables related to vegetation, topographic, anthropogenic, and climate factors, organized into three indices representing spatial, annual-cycle, and lower frequency temporal domains. These indices can interact, so variables promoting fire in one domain amplify fire-promoting effects in another. Fire probability and si..., , **Changes after Aug 2, 2025:** The WULFFSS model was reparameterized in December 2025 due to updates in the observed wildfire dataset and some of the predictor variables. The WUMI2024a dataset of observed wildfires was improved during the review process for the paper describing that dataset (Williams et al., 2025), so we recalculated the records of observed forest fires used to parameterize the WULFFSS model accordingly. Some climate predictor variables were updated due to a recent update of the Rahimi et al. (2022) dynamically downscaled ERA5 climate data, which previously ended in August 2023 but now come through early 2025. Four spatial predictor variables related to gross domestic product were also added in response to a recommendation from a reviewer of the paper describing the WULFFSS model (Williams et al., Under Review).

Rahimi, S., W. Krantz, Y. -H. Lin, et al. “Evaluation of a Reanalysis‐Driven Configuration of WRF4 Over the Western United States From 1980 to 2020.” *Jou...