Short-Term Hydrometeorological Drivers of Wildfires in Italy: Insights from Extreme Value Modeling

Extreme Wildfire Events (EWEs) represent a growing threat in Mediterranean regions, yet their short-term hydrometeorological drivers remain less well constrained than those of more frequent, lower-intensity fires. Improving the discrimination between extreme and non-extreme wildfire behavior is therefore essential for advancing fire prediction, early warning, and risk management. This study investigates whether EWEs differ significantly from non-extreme fires in terms of their associated dynamic meteorological, vegetation, and hydroclimatic conditions, using Italy as a national-scale case study representative of Mediterranean fire regimes.

We analyzed a high-resolution wildfire geospatial dataset from the Italian Civil Protection Department, comprising 106,620 fire events recorded between 2007 and 2022 and a total burned area of approximately 1.37 million hectares (Moris et al., 2024). Fires smaller than 1 ha were excluded. To explicitly account for the contrasting statistical behavior of extreme and non-extreme wildfires, we adopted a two-regime modeling framework: i) the bulk of the burned-area distribution was modeled using Generalized Additive Models (GAMs); ii) EWEs were characterized using an Extreme Value Theory (EVT) framework in which burned-area exceedances above high percentile-based thresholds (90th, 95th, and 99th percentiles) were modeled with a Generalized Pareto Distribution.
Our analysis is supported by the integration of data-cube technology, which enables efficient extraction of high-resolution spatiotemporal data. Meteorological, vegetation, and drought-related variables were extracted at daily and 1 km resolution from the Mesogeos dataset (Kondylatos et al., 2023). Only dynamic variables were considered, including meteorological fields from ERA5-Land; land surface temperature, Normalized Difference Vegetation Index, and Leaf Area Index from MODIS; soil moisture from the European Drought Observatory. The Standardized Precipitation Evapotranspiration Index (SPEI) was additionally included as a complementary indicator of drought conditions.

Results indicate that EWEs are governed by processes that differ fundamentally from those controlling more frequent, lower-intensity fires. By isolating the tail behavior of burned area, the EVT framework reveals the dominant influence of drought intensity, near-surface air temperature, and wind speed under rare but high-impact conditions, relationships that are largely obscured when relying solely on bulk-based models such as GAMs. These findings highlight the importance of explicitly modeling wildfire extremes and provide a robust statistical basis for improving extreme-focused fire danger assessment, early warning, and risk management in Mediterranean regions.

Moris, J. V., Gamba, R., Arca, B., Bacciu, V., Casula, M., Elia, M., Malanchini, L., Spadoni, 481 G. L., Vacchiano, G. and Ascoli, D. (2024) A geospatial dataset of wildfires in Italy, 2007- 482 2022. Technical report, Zenodo.

Kondylatos, S., Prapas, I., Camps-Valls, G. and Papoutsis, I. (2023) Mesogeos: A multi467 purpose dataset for data-driven wildfire modeling in the Mediterranean. Advances in 468 Neural Information Processing Systems 36, 50661–50676.