## 2 Materials and Methods  
### Fire and Meteorological Data as the Input Data for a Machine Learning-Based Hazard Unit  
The historical patterns of wildfires in Southern France were based on the Promethee dataset from 1973 to 2019. The database contains, for each DFCI mesh, several metrics (Table 1). The DFCI geographical mesh system is used in France by actors for fire prevention from the 100-km to the 2-km resolution (Figure 2). Figure 1: Cat model structure used by the (re)insurance sector to estimate the amount of loss due to natural disasters, in this case wildfire events. For the study, machine learning-based methods were integrated into the model, namely into the hazard unit. The latest was based on the use of machine-learning methods to define the number of fire events and the burnt surface. Once the best model for each of the variables was defined, the outputs were combined with the damage unit. The vulnerability unit gathers all of the information about the insured portfolios with building locations and insured values. Then, the damage unit (risk assessment), allows the loss estimation for a wildfire event. The Fire Weather Index (FWI) system was developed by the Canadian Forest Fire Danger Rating System (CFFDRRS) in the seventies [28]. This indicator is used worldwide as a trustable indicator for the study of climate change effects in fire exposure [29]. The indicator is available on the EFFIS Copernicus website throughout Europe. The FWI is calculated daily by Meteo-France for France via Arp\(\acute{\text{e}}\)ge-Climat 4.6 over the period of reference: 1959-2007. The FWI data was downloaded from Dries's Meteo-France platform and for the period 1973-2007. The FWI is only available on a seasonal average from March to November. The higher the FWI is, the greater the probability of wildfire is. The winter season is not studied in this article. The Safran data provide information about the temperature, humidity, wind and precipitation. The data were downloaded from the [PERSON]'s Meteo-France platform. The resolution is 8 km \(\times\) 8 km; for different RCP scenarios, a total of 8602 points cover France. The evolution of the critical meteorological parameters was calculated for 1973-2005, and for the horizon 2050 RCP 4.5 seasonality of the parameters and a 20 \(\times\) 20 km\({}^{2}\) analysis. The study focuses on a seasonal timescale in order to highlight the variation of the meteorological metrics. Climsec Meteo-France data are available as a seasonal average for the entire year. We assume that the fire event within a DFCI mesh is uniquely determined by the mesh's meteorological conditions. EURO-CORDEX (Coordinated Downscaling Experiment) data are available daily by Safran point. The data were reanalyzed in order to obtain them for the same seasonality as the FWI and Climsec data (Table 2). \begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Details** \\ \hline Date & Date of occurrence of the wildfire \\ Number & Id of the fire \\ Type of fire & Unfilled variable \\ Department & Localization of the fire \\ INSEE ID & French ID for community \\ Community & Name of the community \\ DFCI mesh & Id of the DFCI mesh \\ Alert & The data and hour of the first fire alert \\ Origin of the alert & Policemen, population, aerial etc. \\ Burnt surface & In m\({}^{2}\) \\ Max\_burnt\_surf & Maximal burnt surface for each DFCI mesh \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Metrics of the Promethee database used for the study. Figure 2: DFCI mesh, from the national to the local scale ([[https://www.data.gouv.fr/fr/datasets/carroyage-dfci-2-km/](https://www.data.gouv.fr/fr/datasets/carroyage-dfci-2-km/)]([https://www.data.gouv.fr/fr/datasets/carroyage-dfci-2-km/](https://www.data.gouv.fr/fr/datasets/carroyage-dfci-2-km/)), (accessed on 12 October 2020)). The mesh has a value of 100 km with a letter code, exemplified with “LD”. Then, for the 20 km resolution, two figures are added—e.g., “LD26”—and for the 2 km a letter and a figure are added—e.g., “LD26G2”—providing a unique code for each mesh.