- 1Regional Atmospheric Modelling (MAR) Group, Department of Physics, Regional Campus of International Excellence Campus Mare Nostrum (CEIR), University of Murcia ES
- 2Dept. of Applied Mathematics and Computer Science, University of Cantabria ES
- 3Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC) ES
- 4CMCC Foundation - Euro-Mediterranean Center on Climate Change IT
- 5European Centre for Medium-range Weather Forecasts (ECMWF) GB
Accurate seasonal fire predictions can be decisive for mitigating wildfire risks, optimizing firefighting resources, and informing climate adaptation strategies. This study introduces an innovative hybrid approach that combines process-based seasonal climate predictions with a Random Forest (RF) climate-fire model to forecast burned area (BA) anomalies at a global scale. Utilizing the Standardized Precipitation Index (SPI) derived from both observations and ECMWF SEAS5 seasonal predictions, we demonstrate skillful fire forecasts up to four months.
Our findings indicate that observational data allows predictions of BA anomalies in approximately 68% of the burnable area globally, while skillful results are achieved in 46% of the area when incorporating seasonal forecasts. The RF model substantially outperforms traditional logistic regression models, capturing complex, non-linear relationships between climate variables and fire dynamics. The system achieves its highest skill in fire-prone regions, such as Australia and South America, leveraging antecedent and concurrent drought conditions to improve predictability.
This hybrid approach underscores the importance of integrating observational and forecast data to enhance the skill of seasonal fire predictions. By leveraging machine learning techniques, the system provides a flexible and robust framework for developing operational fire forecasts, paving the way for proactive wildfire management strategies under a changing climate.
Acknowledgements:
This work was supported by the project ‘Climate and Wildfire Interface Study for Europe (CHASE)’ under the 6th Seed Funding Call by the European University for Well-Being (EUniWell). M.T. acknowledges funding by the Spanish Ministry of Science, Innovation and Universities through the Ramón y Cajal Grant Reference RYC2019-027115-I and through the project ONFIRE, Grant PID2021-123193OB-I00, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. AP acknowledges the support of the EU H2020 project “FirEUrisk”, Grant Agreement No. 101003890.
How to cite: Turco, M., Torres-Vázquez, M. Á., Herrera, S., Gincheva, A., Halifa-Marín, A., Cavicchia, L., Di Giuseppe, F., and Montávez, J. P.: Hybrid Climate-Fire Models for Better Seasonal Wildfire Predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5938, https://doi.org/10.5194/egusphere-egu25-5938, 2025.
