Spatio-Temporal Projection of Forest Fire Risk in the Aegean and Mediterranean Basins of Türkiye (2026–2096)

The number of wildfires along the Mediterranean and Aegean coasts increases each year, impacting regional industries and ecosystems. In particular, the wildfire that occurred in Izmir, located in western Türkiye, on June 29-30, 2024, with peak temperatures exceeding 40°C and wind gusts reaching 22 m/s, spread to residential areas, resulting in the temporary closure of the city's airport and disrupting aviation operations. Therefore, predicting regional fire hazard risk based on meteorological data has become crucial, and many studies have been conducted in this area. The Canadian Fire Weather Index System (FWI) estimates forest fires based on the effect of fuel moisture and weather conditions. In this work, the risk of forest fires in Türkiye's Aegean and Mediterranean coastal regions has been estimated for future years using FWI data produced using high-resolution regional climate models supplied by the Copernicus Climate Change Service. The future years between 2026 and 2096 were compared under optimistic (RCP 2.6), moderate (RCP 4.5), and pessimistic (RCP 8.5) emission scenarios, with the 1971–2005 reference period. The results of this study showed that the number of extreme risk days (FWI > 45) increases from 50.48 days to 55.22 days (9.4% increase) under the RCP 2.6 scenario, to 57.26 days (13.4% increase) under the RCP 4.5 scenario, and to 61.71 days (22.2% increase) under the RCP 8.5 scenario when compared to the reference period. More significantly, according to the RCP 8.5 scenario, the risk level in coastal regions is estimated to reach 234.92 days annually, meaning that the risk of fires along the Aegean and Mediterranean coasts may last almost 65% of the year. In order to manage fire hazards in the Aegean and Mediterranean regions, where the risk of fire is extremely high, strategies that prioritize low-emission policies and carefully regulated tourism activitiesare crucial, as evidenced by the difference between RCP 2.6 and RCP 8.5 scenarios. The RCP 8.5 scenario also confirms that heat waves and altered precipitation patterns have increased the frequency and severity of these risks. These results indicate that the fire hazards will increase in the future, highlighting the importance of detailed information on fire risk assessment over the coastal areas of Türkiye’s Aegean and Mediterranean regions. In this context, the next phase of this study will focus on utilization of a Random Forest-based Inference Engine model to increase 12.5 km resolution of the EURO-CORDEX data to a 1 km spatial resolution in order to improve fire risk assessment. The model aims to identify non-linear wildfire risk patterns by correlating FWI components with local geographic features using an ensemble of decision trees. The proposed system is intended to operate as a Decision Support System (DSS) by automatically classifying extreme weather clusters, providing real-time resource allocation strategies.