High-resolution Probabilistic Forecasts of Fire Weather Conditions in California using Downscaling Machine Learning Models

California is prone to extreme fire weather conditions characterized by high winds, elevated temperatures, and low humidity. Accurate predictions with high spatial resolution are critical for emergency operations to monitor and respond to fast-spreading wildfires. While current operational numerical weather prediction models, such as the NOAA Global Forecasting System GFS model, offer reliable probabilistic forecasts in the medium range (up to 15 days), their coarse spatial resolution (typically 0.25° latitude/longitude, ~25 km) limits their utility for localized fire risk assessment. This resolution is insufficient for capturing terrain-driven wind patterns and microclimate variations that drive fire behavior, especially in complex topography regions like the wildland–urban interface.

High-resolution probabilistic forecasts of fire weather conditions are generated by downscaling GFS ensemble outputs from a native resolution of 0.25° latitude/longitude to 1.5 km horizontal grid spacing over a domain encompassing California and Nevada. The downscaling framework integrates singular value decomposition (SVD), UNet-based convolutional neural networks, and diffusion models to capture both large-scale variability and fine-scale terrain-driven features. Models are trained using GFS initial conditions (00 UTC) and paired with 1.5 km Weather Research and Forecasting (WRF) simulations spanning the period 2015–2020. To evaluate forecast skill, ten high-impact case studies characterized by strong wind events in the Sierra Nevada and Southern California are analyzed. Probabilistic predictions of surface air temperature, relative humidity, and wind speed are validated against surface meteorological observations. The study includes a discussion of forecast skill metrics, operational applications, and ongoing research directions.