The 2025 Los Angeles wildfires highlighted growing risks from climate-driven extremes and the need for reliable wildfire forecasting beyond short lead times. Although the European Centre for Medium-Range Weather Forecasts provides global fire weather index forecasts, these products are primarily designed for long-range climate outlooks, and their practical effectiveness remains uncertain in regions with limited local forecasting infrastructure. Here we show that a global deep learning framework, forecasting daily fire weather index values up to 31 days ahead, consistently improves forecast accuracy and reduces bias relative to operational numerical forecasts. The framework learns nonlinear and lagged fire–weather relationships by integrating past fire weather index dynamics and future meteorological conditions. Importantly, forecast bias is reduced in 85% of grid cells where high wildfire exposure coincides with high socioeconomic vulnerability. This demonstrates that data-driven forecasting can help bridge critical information gaps in underserved regions and support more equitable climate risk management. A new deep learning-based framework shows improved medium-range predictability for global fire weather forecasts compared to traditional forecast products by learning nonlinear and lagged fire-weather relationships, especially in vulnerable regions.
Kang et al. (Thu,) studied this question.