Future wildfire risk in Southern Europe under changing land use and climate scenarios

Wildfires are among Europe’s most damaging natural hazards, with significant impacts on ecosystems, economies, and society. Assessing how wildfire risk may evolve under climate change remains challenging, as fire occurrence depends not only on meteorological conditions but also on topography, land use, and human activity. However, most future-oriented studies rely on traditional weather-based indices, such as the Fire Weather Index, which do not explicitly account for these additional drivers. Machine-learning (ML) approaches offer a powerful alternative by integrating multiple sources of information, yet their application to future wildfire risk under combined climate and land-use change scenarios remains limited.

In this study, we develop a data driven ML wildfire risk model for Southern Europe trained on historical data. Several ML algorithms are evaluated, with XGBoost (XGB) model having the best performance (AUC = 0.93; F1 = 0.83). Explainable AI techniques are used to interpret model behavior and identify the most influential predictors of wildfire risk.  The trained model is then applied to future climate projections using a regional multi-member ensemble of the Canadian Regional Climate Model version 5 (CRCM5) covering the European CORDEX domain at a high spatial resolution (0.11°, 12 km). Wildfire risk is investigated under the Shared Socioeconomic Pathways SSP1-2.6 and SSP3-7.0. Simulations driven exclusively by greenhouse gas (GHG) forcing are compared with simulations that also incorporate land-use change (LUC). Future projections indicate an increase in wildfire risk by the end of the century (2081–2100), under the SSP3-7.0 scenario, with a stronger rise when including both LUC and GHG changes compared to the one including GHG alone. These findings show the important role of land-use change in shaping future wildfire risk and highlight the need of integrating socio-environmental drivers along with climate change in wildfire risk assessments.