Bridging the “Last-mile Gap” in Climate Services Delivery: A Dynamical-AI Hybrid Framework for Next-Month Wildfire Danger Prediction and Emergency Action

Abstract Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses, yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers. This study introduces an innovative hybrid modeling framework that integrates artificial intelligence (AI) with climate dynamic prediction systems to accurately forecast High Fire-Danger Days (HFDDs) for the following month. These HFDDs are derived from historical satellite fire data and the optimum fire danger index, with a particular focus on Southwest China as a case study. The AI module, based on the ResNet-18 neural network model, integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months. Leveraging climate dynamical forecasting, this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation. More importantly, the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs, facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’ needs. The model’s added economic value was also evaluated, demonstrating its potential to improve decision-making in disaster management and bridge the “last-mile gap” in climate service delivery. This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment (SEPRESS) Program (2025–32), under the United Nations Educational Scientific and Cultural Organization (UNESCO) International Decade of Sciences for Sustainable Development (2024–33).