We used the Moderate Resolution Imaging Spectroradiometer (MODIS) Firecci v5.0 product developed within the framework of the European Space Agency's Climate Change Initiative (CCI) program and available in the period 2001–2016 . This product is based on MODIS on board of the Terra polar heliosynchronous orbiting satellite. The burned-area algorithm combined temporal changes in near-infrared MERIS-corrected reflectances based on MOD09GQ of the MODIS sensor at 250 m spatial resolution with active fire detection from the standard MODIS thermal anomalies product, following a two-phase algorithm . Complementary to the surface reflectance product, the daily MOD09GA Collection 6 product was also used to extract information on the quality of the data. Although small wildfires are generally difficult to detect with satellite observations due to the timing of the scan or cloud-cover impairment of remote sensing, our focus on large wildfires is expected to minimize this uncertainty.

We excluded MODIS fires located within agricultural lands using CORINE Land Cover 2012 data (https://land.copernicus.eu/pan-european/corine-land-cover, last access: 28 Febuary 2019) as well as prescribed fires related to pastoral practices during the cool season (November–March), since these fires are generally under control and do not put infrastructure or ecosystems at risk. Spatially and temporally adjacent MODIS pixels were aggregated using the location and the date of the first detection to form consistent wildfire events. Pixels belonging to the same wildfire event were required to be within a maximum distance of 4 pixels (to minimize inaccuracies in burned-area detection within a pixel) and to have adjacent burning dates. The 22 785 MODIS pixels extracted from 2001 to 2016 across France were found to form 894 distinct wildfire events. We then defined large wildfires as exceeding 100 ha (N=156 large wildfires) following , a threshold corresponding here to the 83th percentile of the distribution of wildfire area. The average large-wildfire area was found to be 398 ha, with the largest wildfire reaching 7675 ha. Finally, we regridded this information onto an 8 km resolution grid to facilitate the comparison with meteorological data (see Sect. 2.2).

Meteorological variables were obtained from the quality-controlled SAFRAN (Système d'Analyse Fournissant des Renseignements Atmosphériques a la Neige; Analysis system providing data for the snow model) reanalyses, providing minimum and maximum temperature, relative humidity, precipitation, and wind speed over France from 2001 to 2016 on a daily basis and over an 8 km grid.

Drawing from the SAFRAN data, we derived a suite of fire-weather and drought indices (see Table 1) intended to reflect different timescales of variability that are widely thought to facilitate wildfire spread from synoptic (weather) to interannual (climate) scales . Fire-weather variables included the Canadian Forest Fire Weather Index system (fine fuel moisture code (FFMC), build-up index (BUI), duff moisture code (DMC), initial spread index (ISI), drought code (DC), and fire-weather index (FWI)) . Although these indices were empirically calibrated for estimating whether atmospheric conditions and fuel moisture content are prone to wildfire development in a jack pine forest of Canada , the FWI system has proven useful in Mediterranean regions as well as in Alpine environments . We also included in our analysis other fire-weather indices that have been shown to be useful for estimating fire danger conditions across parts of the world including the Forest McArthur Fire Danger Index , the F-Index , the Nesterov Fire Danger Index (Nesterov, 1949) and the Fosberg Fire Weather Index (Fosberg, 1978). Further information on each of these fire-weather variables and how they relate to wildfire activity can be found in the literature.

Additionally, we used a series of fast- and slow-reacting drought indices to detect flash and chronic droughts that are often associated with large wildfires. These indices include potential evapotranspiration (PET) based on the Penman–Monteith equation, the effective drought index (EDI) , here integrating precipitation over the last 30 days to detect a short-term precipitation deficit, and the standardized precipitation index (SPI) based on a nonparametric framework and computed on 6-month windows to detect a long-term precipitation deficit. The SPI has already shown some skill in predicting burned area across different parts of the globe . Finally, we used the more sophisticated soil wetness index (SWI) developed by CNRM (Centre National de la Recherche Météorologique). This last index was derived from ISBA , a soil–biosphere–atmosphere interaction model based on soil characteristics across France, reflecting the moisture available for the plants. The SWI integrates the propagation of moisture from the superficial surface layer to the root zone .

The relationship between weather-to-climate and wildfire activity in France is mediated through vegetation and the complex topography of the region (Fig. 1a) alongside human factors . Given the compounding influence of these environmental factors, we developed separate models using an environmental stratification (Fig. 1b) based on climate data, topography, and geographical position , assuming that the weather-to-climate forcing on large wildfires is relatively consistent within each of these regions. Only a brief description of these environmental units is given here. The northern region (Atlantic central in ), the less fire-prone region (Table 2), corresponds to a temperate climate in which average summer temperatures are relatively low. The Alpine region spans high-mountain conditions typical of the alpine ranges of southern Europe, which are dominated by conifer forests at high elevation and broadleaf forest at low elevation. The western region (Lusitanean in ), corresponds to the southern Atlantic climate characterized by warm and dry summers coupled with mild and humid winters. Further south, the Mediterranean area is stratified into three distinct environmental regions: the Mediterranean mountains (labeled “Mnts” in the figures), which combine the influence of both Mediterranean and mountain climates (including various species such as Fagus sylvatica, Pinus sp., Quercus pubescens), the Mediterranean north, which contain holm-oak- and cork-oak-dominated vegetation (Quercus ilex, Quercus suber, Pinus sp.) and the Mediterranean south, a low-elevation area (Fig. 1a) spanning the Rhône delta. The spatial extent of each region allows for the pooling of a decent number of MODIS wildfires needed to develop robust models. We however acknowledge the existence of subregional variations in human factors (e.g., ignition and suppression) and that other biogeographic units with homogeneous attributes with respect to wildfire regime and climatic conditions may yield different results .

In each region, the annual frequency of large wildfires strongly shaped interannual variations in annual burned area (Fig. 2), with an overall contribution in total burned area from 2001 to 2016, ranging from 62 % (north) to 84 % (Mediterranean mountains) (Table 2). Large wildfires in the Mediterranean north and Mediterranean mountains were the strongest contributors to national total burned area (Table 2). Note that the Mediterranean mountain region has experienced a dramatic increase in large wildfires and annual burned area in response to the 2003 heatwave. It is thus readily apparent that a few large wildfires are responsible for the majority of the burned area and that a better understanding of drivers and processes of these specific events is of utmost importance.

Most models showed high skill (Fig. 3). The area under the curve (AUC) was the highest for the Mediterranean north, which is also the region with the largest sample of large wildfires, while the Mediterranean south model had the lowest predictive power. In this region, a true-positive rate hardly exceeding 0.8 was associated with a false-negative rate exceeding 0.7, indicating that large wildfires in that region are less related to the weather-to-climate forcing.

Table 3 shows the best model selected in each environmental region alongside the relative model selection frequencies (πi) from applying BIC to each of the 1000 bootstrap samples. In five out of the six regions, models were selected as the best in >90 % of the simulations. However, the Mediterranean mountains model had much lower selection frequencies (36 %). Note that a lower frequency of selection does not mean that the model has a lower skill, but rather that other combinations of predictors are possible. The table also shows the different predictor variables selected in the stepwise procedure and indicates how the weather-to-climate forcing can affect large wildfires very differently depending on what kinds of environmental conditions predominate, as already shown in previous works across the USA . Table A1 in Appendix A provides the typical range of each explanatory variable on days with large wildfires. In the north, the best model uses only FFMC, an index reflecting the flammability of litter and fine fuels. As opposed to Mediterranean regions, where FFMC quickly saturates in early summer due to overall low soil moisture conditions, this index seems to be useful for tracking large-wildfire potential in more humid climates where fine fuels dominate. By contrast, the best models for Alpine and western regions are based on slow-reacting indices (SPI for the Alpine region and DC for the western region), both reflecting chronic soil moisture deficit and low fuel moisture levels . This suggests that large wildfires in these more humid and more flammability-limited systems are mainly enabled by slow-evolving drought. The picture is slightly more complex in both the Mediterranean mountains and Mediterranean north. For instance, the best model in the Mediterranean north combines the information provided by the FWI (fire-weather metric) with the SWI (soil moisture content metric). In fact, a strong decrease in the SWI in summer, corresponding to a reduction in plant-available soil moisture level, accelerates the desiccation and may lead to vegetation mortality , which in turn facilitates wildfire spread. Besides, an increase in the FWI, which also integrates the expected rate of spread in response to wind speed, once again underlines the strong role of wind speed in wildfire spread in the Mediterranean north. In other words, large wildfires occur when multiple conditions are gathered, namely high winds, dry fuel and low soil moisture levels. This illustrates how complementary fire-weather and soil moisture indices are, and how they may, collectively, improve the ability to track the potential for large wildfires.

It is noteworthy that the effect of wind speed on large wildfires is only revealed through the FWI in the Mediterranean north. The absence of wind speed as a significant factor in other regions may arise due to the temperature decrease associated with wind spells in the French Mediterranean , with contrasting effects on commonly used fire-weather indices that were designed to increase with temperature. This may also indicate the stronger role of fuel moisture in these regions in response to slower climatic variations, regardless of what short-term fire weather does.

The mean seasonal cycle, featuring a peak in August in most regions, is well reproduced in the simulations (Fig. 4 and Table 4). However, some large wildfires were also seen in the spring and in September in the north and Alpine regions respectively, a feature that is not reproduced in the model either due to sample size limitations or other human factors not included in the models. Figure 5 shows the spatiotemporal patterns of mean daily simulated probabilities from May to October. The potential for large wildfires emerges in the Mediterranean south first and then propagates northwards into the Mediterranean mountains and along the west coast before slowly decaying in October. An animation of daily simulated probabilities from 2001 to 2016 is available in the Supplement (VideoS1.mov).

Models were also able to simulate, to some extent, interannual variations in large wildfires (Fig. 6 and Table 4), including the exceptional 2003 outbreak in the Mediterranean mountains. As expected, interannual variance in simulated probabilities was much lower than that of large wildfires observed (Fig. 6) due to the continuous nature of probabilities (in contrast to the strongly intermittent nature of large wildfire), thereby underestimating (overestimating) the probability of very likely (unlikely) events and resulting in a variance deflation.

Figure 7 shows simulated probabilities expressed as anomalies with respect to the mean local seasonal cycle during a period spanning 80 days before to 80 days after large wildfires pooled over the entire country. It is readily apparent that simulated probabilities progressively increased until the days with large wildfire, reaching values 2–3 times higher than normal and then slowly decaying towards normal conditions. This temporal pattern was, however, variable across environmental regions (Fig. 8) depending on the predictor variables selected (Table 2). In fact, the slowly increasing probabilities evident in the Alpine region and the Mediterranean mountains mimic the slow variations of the SPI and the SWI, respectively, and align with global change-type drought . By contrast, faster-increasing probabilities in the Mediterranean north on days with large wildfires reflect the role of short-term fire-weather conditions, again highlighting different generating mechanisms across the regions.

This modeling framework has multiple potential applications. First, it could be implemented in a real-time fashion using meteorological forecasts. This may complement traditional forecasts based on FWI only. Indeed, the FWI only measures the potential intensity of wildfire and this quantity is not always straightforward in the real world. In this regard, our model translates a series of fire-weather and drought indices into a probability of the occurrence of large wildfires that could be useful in decision-making. Second, our model may serve as a basis on which to simulate future changes to large wildfires based on future climate projections from the EURO-Cordex project. Such projections will help better understand future changes and will provide the information decision makers need for successful adaptation to climate change.

However, several caveats and well-known limitations apply to our modeling framework. First, our model is based purely on weather and climate and ignores human activities (ignition or suppression). Indeed, drought is one component of a complicated wildfire system and our modeling framework is obviously contingent on ignition and fuels. Although human activities add a less understood and therefore less predictable component , including human factors . as well as causes of wildfire ignition may improve model skill. All these factors should be considered in the more complex context of risk assessment. Second, the environmental stratification used here has proven useful to aggregate large wildfires and develop different models (and should be considered in future pan-European climate–wildfire modeling efforts) but we acknowledge that this stratification is likely, as is any other stratification, to mix up large wildfires with different human causes and different climate drivers. Third, information on fuel connectivity could also improve model skill and help target the regions at risk. A recent study has shown that wildfire spread in the French Mediterranean is severely limited by fuel connectivity in some regions . Finally, developing a statistical model with a very limited number of large wildfires is a major limitation in some regions. This limitation is obviously inherent to the modeling of extreme events and was partly overcome with the use of a resampling approach. Nonetheless, a longer record of wildfires would certainly allow for the stabilization of the equations.