{"paper_id":"3613fe6e-e962-4074-a100-2efd629d3127","body_text":"Human-induced climate change intensifies spatially compounding fire weather extremes across European countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Human-induced climate change intensifies spatially compounding fire weather extremes across European countries Emilie Gauthier, Emanuele Bevacqua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8583363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2026 Read the published version in npj Natural Hazards → Version 1 posted 11 You are reading this latest preprint version Abstract Intensifying fire-weather extremes increasingly threaten Europe, with recent wildfires linked to human-induced climate change. Yet, little is known about spatially compounding fire danger events—days when multiple regions simultaneously face extreme fire weather—which can trigger widespread fires and potentially overwhelm shared EU firefighting resources, amplifying impacts. Here, we analyse spatially compounding fire danger by combining burned area observations (2001–2015), ERA5-based Fire Weather Index (1950–2024), and CMIP6 climate simulations. We reveal that cross-country correlations in fire weather strongly enhance the likelihood of extremely widespread fire weather, with long-lasting compound hot-dry conditions acting as key meteorological drivers. The spatial extent of extreme fire weather has expanded markedly over the past three decades, primarily due to rising temperature and the associated decline in relative humidity. On average over the past decade, human-induced climate change contributed to the annual-maximum extent of European land synchronously experiencing extreme fire weather by 14.8% (4.8–25.6%, interquartile range across models). These results highlight the need for coordinated European adaptation to the growing potential for large-scale wildfires. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Wildfires are a natural component of many vegetated ecosystems, yet they also rank among the most destructive climate-related hazards 1 , 2 . In recent years, the European Union (EU) has seen a substantial rise in burned area 3 – 5 . Indeed, 2025 has been the worst year on record, with widespread wildfires and over 1 million hectares burned 6 . To respond more effectively to disasters such as wildfires, 37 countries cooperate through the EU Civil Protection Mechanism, which enables the sharing of firefighting aircraft, personnel, and medical support 7 . However, when spatially compounding fires 8 , 9 occur––events that simultaneously affect multiple regions—the accumulated damage can be substantial, and the shared capacities of the mechanism can be overloaded, limiting response action and potentially enhancing impacts 10 – 12 . For example, in 2021, fires occurred in 22 European countries, creating unprecedented strain on firefighting services 13 . In 2025 alone, the mechanism was activated 18 times in 11 countries, deploying 58 aerial firefighting means 14 . Wildfires accounted for nearly 20% of all assistance requests to the EU mechanism between 2007 and 2024 14 , underscoring the need to gain a better understanding of spatially compounding fire danger events. Given fuel and ignition, compound hot, dry and windy weather conditions strongly influence fire occurrence, behaviour and impacts 15 , 16 . Such weather conditions are integrated into the concept of fire weather, a term encompassing key meteorological drivers like temperature, relative humidity, wind, and precipitation 17 . Fire weather controls the timing and spread of fires, and also regulates much of the interannual variability in burned area 18 – 20 . While various indices have been developed, one of the most widely used is the Canadian Forest Fire Weather Index (hereinafter FWI), which integrates key meteorological drivers into six subcomponents representing fuel dryness, fire spread, and ignition potential 21 . The FWI correlates well with observed burned area over Europe 18 , 22 , 23 and is used operationally by the European Forest Fire Information System (EFFIS) to monitor fire danger 24 , making it a relevant index for analysing spatially compounding fire danger. Across the continent, climate change is projected to intensify FWI extremes, particularly in southern Europe 25 , 26 , with rising temperatures and decreasing relative humidity contributing to longer fire seasons and more frequent FWI extreme events 23 , 25 – 35 . Despite the intensification of FWI extremes, little is known about spatially compounding fire danger in Europe, defined as days when a large extent of European land experiences extreme FWI conditions simultaneously. This gap arises as fire danger has traditionally been assessed at individual locations, without considering the spatial interplay or correlations in FWI across regions 36 . For the United States, Abatzoglou et al. (2021) showed that days of synchronous fire danger closely align with periods of intense strain on national fire management resources 36 . Similarly, Richardson et al. (2025) showed the potential for increased challenges in firefighting cooperation across the United States, Canada and Australia 37 . Torres-Vázquez et al. (2025) analysed historical trends, and future projections of synchronous FWI extremes for individual European IPCC regions under synthetic, spatially uniform temperature and precipitation trends, identifying Central Europe as a hotspot of increasing synchronicity 38 . However, their framework 38 does not explicitly account for relative humidity, thereby neglecting the indirect effects of temperature on atmospheric dryness through changes in moisture-holding capacity. Overall, the contribution of trends in multiple FWI drivers to spatially compounding fire danger remains unclear. This includes the role of temperature trends and its impact on relative humidity, the physical drivers of individual extreme compounding events, and the influence of human-induced climate change on recent trends. Moreover, while correlations among compounding drivers are known to affect risk 39 – 41 —meaning that cross-country FWI correlations may increase or decrease the likelihood of widespread fire danger events across Europe—the impact of such spatial correlations remains unexplored. To provide a systematic assessment of spatially compounding fire in Europe, we investigate extreme fire danger across countries participating in the EU Civil Protection framework (hereinafter referred to as Europe). Using the ERA5 climate reanalysis 42 , we calculate the FWI for the fire season (May to October, see Supplementary Fig. 1). We then combine it with observed burned area (BA) from the GFEDv4.1 Database 43 to inspect the relationship between historical BA events and FWI conditions, and then analyse the effects that spatial correlations have on spatially extended FWI extremes. An event-based approach is employed to investigate the key meteorological anomalies underlying extreme spatially compounding fire danger events. To assess how climate change has shaped spatially compounding fire danger in recent decades, we quantify long-term trends in the FWI and isolate the contributions to these trends from key meteorological variables by using ERA5 and climate models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) 44 . Results European burned area rises with the spatial extent of FWI extremes The Fire Weather Index (FWI) is a cornerstone fire danger indicator in Europe, and the European Forest Fire Information System (EFFIS) operationally defines extreme fire danger when the FWI exceeds 50 24 . Building on this operational definition of an absolute FWI threshold—which we further supported by exploring the BA-FWI relationship across multiple FWI thresholds (see Methods)—we employ the daily extent of European land experiencing FWI ≥ 50 simultaneously for characterising the intensity of spatially compounding fire danger. By combining this metric with daily BA data, we find that the more widespread the spatially compounding fire danger, the larger is the European-total BA. Specifically, by grouping daily European-total BA by the extent of land under FWI ≥ 50, we observe a clear shift of the BA distribution toward higher values as the extent increases (Fig. 1 ). In other words, the larger the land simultaneously experiencing extreme fire weather, the more likely Europe is to record very high BA totals. Although wildfire occurrence also depends on ignition sources and fuel availability, these results indicate that FWI provides valuable insight into spatially compounding fire danger, which is consistent with earlier studies 18 , 19 . Cross-country spatial correlations of extreme fire weather enhance spatially compounding fire danger To quantify the influence of cross-country correlations in FWI on spatially compounding fire danger, we compare the distribution of the European land simultaneously experiencing extreme FWI in observed versus spatially shuffled data during the fire season (see Methods) (Fig. 2 ). Our results reveal that cross-country correlations in FWI substantially amplify extreme events characterized by a large extent of European land simultaneously experiencing extreme FWI. For example, the likelihood of a day when at least 13% of European land is under extreme FWI is 56 times higher than expected in the absence of spatial correlations during the fire season. Overall, these results demonstrate that, due to spatial correlations in FWI, multiple European countries tend to experience extreme FWI simultaneously, which can enhance the potential for widespread fires. Having established that spatial correlations amplify the probability of widespread extreme fire weather, we now investigate the local-scale atmospheric anomalies that trigger the most extreme widespread events. To do so, we focus on the ten days between 1950 and 2024 with the largest annual-maximum extent of European land experiencing extreme FWI, which mainly affected southern and central Europe (Fig. 3 a). Extreme FWI rarely occur due to short-term weather anomalies in isolation, but rather emerge from a progressive build-up of meteorological anomalies 21 . On average, positive FWI anomalies emerge up to about two months before the event and typically intensify markedly around 10 days before the event (Fig. 3 b), closely tracking a similar rise in temperature anomalies (Fig. 3 c). Negative anomalies in precipitation and relative humidity dominate the pre-event period, with relative humidity reaching its minimum on the day of the extreme, consistent with its drying effect on fuel moisture 21 . Wind speed anomalies are less systematic, though positive anomalies are observed on the event day, in line with wind’s effect on the pace of fire spread 21 . Altogether, these patterns provide new insights into the evolution of local extreme fire weather during spatially compounding events in Europe, highlighting the role of past weather anomalies in building up extreme fire danger. Temperature and relative humidity as main drivers of observed long-term trends synchronous fire weather Expanding the scope beyond single events to better understand the long-term evolution of spatially compounding fire danger and its underlying drivers, we first analysed trends in FWI and its key meteorological drivers averaged across Europe during the fire season using ERA5 reanalysis (Fig. 4 a-e). Averaged daily FWI shows relatively high values in the 1950s–1960s, followed by a decrease until around 1970, and a renewed increase in recent decades. Among FWI drivers, the annual averaged daily maximum temperature exhibits a pronounced upward trend since the 1980s, in line with the intensification of heat extremes across Europe 45 . Daily minimum relative humidity displays an increase until about 1980, followed by a persistent decline thereafter. Mean monthly precipitation increased slightly during 1950–1970 but has remained relatively stable since, while wind speed shows no clear long-term trend. ERA5-based trends are consistent with independent observational datasets such as the Ensemble of daily precipitation and temperature records for Europe (E-OBS) (Supplementary Fig. 2) and corroborate previous findings from the literature 46 , 47 , strengthening confidence in the observed large-scale weather changes. To assess spatially compounding fire danger, we inspected the annual-maximum extent of the European land simultaneously exposed to extreme FWI (Fig. 4 f). In line with the FWI averaged across Europe (Fig. 4 a), this spatial extent was relatively high in the 1950s, declined in the 1960s–1970s, and increased again from the 1980s onward. While the long-term evolution of the mean weather drivers (Fig. 4 b-e) offers an initial indication of their relative influence on spatially compounding fire danger trends, quantifying the individual effect of trends in weather drivers requires further analyses. To this end, we detrended each FWI input variable separately and recomputed the FWI (see Methods). Comparing the annual-maximum extent of European land under extreme fire danger between the original and detrended simulations allows to isolate the contribution of each driver (Fig. 4 g; note that the annual maxima are identified independently in each simulation and can therefore occur on different calendar days across simulations). We find that relative humidity largely explains the temporal evolution of the spatial extent of FWI extremes in the mid-20th century, whereas in recent decades increasing temperatures and decreasing relative humidity expanded the area affected by FWI extremes. While the decomposition reveals that relative humidity and temperature are the main contributors to FWI trends over Europe, these two variables are physically linked, as relative humidity represents the ratio of actual specific humidity to saturation specific humidity—the latter being primarily controlled by temperature (as saturation specific humidity increases with temperature) and, to a lesser extent, by pressure 48 . This implies that higher temperature reduces relative humidity—provided that specific humidity does not increase at the same rate. Thus, following previous work 35 , we detrended relative humidity to disentangle the contributions of temperature and specific humidity (Fig. 4 h). Specific humidity has increased since 1950, leading to a rise in relative humidity during the mid-20th century as this increase was not yet counteracted by the increasing temperature. After around 1980, however, this increase has been outweighed by the strong warming, which, by increasing saturation specific humidity, led to a decline in relative humidity. Such a decomposition further allows for quantifying the overall contribution of temperature trends to FWI extremes, after accounting for both its direct effect and indirect effect via relative humidity (Fig. 4 g, orange dashed curve). This demonstrates that rising temperature is a key driver of the recent increase in the spatial extent of extreme fire danger events across Europe. Overall, these findings highlight that the observed increases in the extent of widespread fire danger events across Europe are largely driven by rising temperatures and decreasing relative humidity. Human-induced climate change amplifies synchronicity in wildfire-prone weather The detected observed trends alone cannot inform on the total contribution of human-induced climate change, which requires information on the pre-industrial climate. Furthermore, internal climate variability can substantially influence observed trends, challenging the isolation of human-induced climate change from observed trends 49 . We therefore used CMIP6 models to detrend each FWI input variable based on a pre-industrial reference period to assess the total human-induced climate change effect 44 , 50 (see Methods). Based on the multimodel mean — which reduces noise from internal variability and averages forced signals across models 51 — we estimate that human-induced climate change contributed, on average over the last decade, to 14.8% (4.8–25.6%, interquartile range across models) of the annual-maximum land surface synchronously affected by extreme FWI. About 76% (13 out of 17) of the models agree on the positive contribution (Fig. 5 a). This corresponds to an increase of nearly 11 900 000 square kilometers of land surface (1.9% of the European land) synchronously affected by extreme FWI. In line with ongoing warming over Europe (Fig. 5 c), temperature was the dominant driver of this human-induced climate change effect, with all climate models (Fig. 5 a) and all 163 ensemble members (not shown) agreeing on its positive influence (Fig. 5 d). Precipitation trends also slightly enhanced spatially compounding fire danger, whereas relative humidity effects—although positive contributions being stronger than those from precipitation—are more uncertain. In contrast, the influence of wind is estimated to be negligible. Note that the magnitude of the warming influence varies over time consistently across model ensemble members (Fig. 5 d), in line with a non-linear response of spatially compounding fire danger to warming. Overall, even more robust patterns emerge when examining the temporal trend of these contributions during 1994–2024 (Fig. 5 b), with a human-induced climate change increase in spatially compounding fire danger of 0.47% per year and 90% (15 out of 17) of the models agreeing on the positive trend. Also, for contributions to these trends, temperature and relative humidity consistently emerge as the main drivers. Finally, we note that the model spread (Fig. 5 a,b) reflects both internal climate variability and model differences in the representation of the forced signal. Discussion Recent decades have seen a rise in large-scale wildfires across Europe 3 – 5 , posing growing challenges to shared-response mechanisms such as the European Civil Protection Agreement 38 . By combining burned area data, climate reanalyses, and CMIP6 model simulations, our study shows that days of highest total burned area over Europe are associated with widespread extreme fire weather conditions. Over the last three decades the annual-maximum extent of European land simultaneously exposed to extreme fire weather has increased markedly, indicating a growing concurrent exposure of European countries to fire-conducive weather. While anomalies in multiple weather variables contribute to individual extreme events, temperature and relative humidity stands as the primary drivers of historical trends in spatially compounding fire danger. In addition to directly increasing FWI, rising temperatures have enhanced the atmosphere’s moisture-holding capacity, thereby favouring a decline in relative humidity, which in turn has led to higher FWI 11 , 21 , 22 . Consistent with these trends, human-induced climate change has contributed, on average over the last decade, to about 15% of the annual-maximum extent of the European land synchronously experiencing extreme FWI conditions. Our findings on spatially compounding fire danger are strengthened by previous studies focussing on fire danger at individual locations, which also identified temperature and relative humidity as the key drivers of increasing FWI 26 , 29 . The fact that the spatial extent of extreme FWI conditions is enhanced by cross-country FWI correlations indicates that large-scale atmospheric conditions play a key role in shaping spatially compounding fire danger. Since extreme FWI occur under high temperature and low relative humidity anomalies—with these anomalies being typically linked to persistent anticyclonic circulation—our results suggest that widespread FWI events are associated with such large-scale, high-pressure regimes 17 , 52 , including atmospheric blocking and ridging 22 , 53 , 54 . However, we also note that the effects of cross-country spatial correlations on European-total burned area are less pronounced, although cross-country correlations affect the number of countries having simultaneously at least 0.01% of their territory burned (Supplementary Fig. 3). The difference in cross-country correlations effects between FWI and burned area aligns with the fact that FWI extremes favour fires, but wildfire events are also strongly modulated by human factors (such as ignition patterns and suppression practices), and availability of fuels 12 . Nevertheless, cross-country correlations in FWI imply that multiple countries often experience extreme fire weather synchronously, indicating the relevance of accounting for spatial dependencies in fire danger assessments and the design of pan-European early-warning systems 50 . In line with the so-called delta approach 55 – 57 , to quantify the contribution of climate variables trends to spatially compounding fire danger, we detrended individually each meteorological input. While different approaches exist for creating counterfactuals, each with its own advantages and limitations 58 , the time series of FWI computed from detrended meteorological drivers shows strong agreement with the detrended FWI, supporting our detrending approach (Supplementary Fig. 4). The effectiveness of our detrending of FWI based on season-specific three-month moving windows to study FWI long-term trends aligns with the fact that FWI is largely influenced by weather anomalies over months (Fig. 4 ), due to the well-known memory of the index 21 . However, we also note that the influence of wind trends should be interpreted with caution, as wind mostly modulates FWI at short timescales. While using climate models is key for attribution studies, models may have biases 46 , 58 and we find that while CMIP6 models capture European temperature and precipitation trends 46 , they underestimate the observed decline in relative humidity 59 (and tend to overestimate decreasing wind speed 60 ) (Supplementary Fig. 5). Such an underestimation of the relative humidity decline implies that our estimated amplification of spatially compounding fire danger from human-induced climate change may be conservative. This aspect is particularly relevant as the decline in relative humidity is strongest across southern and central Europe, where it directly influences spatially compounding fire danger (Supplementary Fig. 6). Reinforcing the conservative interpretation, the only four out of seventeen models that indicate a reduction in spatially compounding fire danger under climate change (Fig. 5 a) are among those that miss most the observed decline in relative humidity. Our findings demonstrate the growing influence of human-induced warming on widespread spatially compounding fire danger events, primarily due to warming and declining relative humidity. Combined with projections of continued warming that will further intensify extreme fire weather 26 , our results indicate that these spatially compounding events may expand further in the future. Regions such as Western and Central Europe, historically less affected by extreme FWI conditions, may be drawn more frequently into large-scale events as the spatial extent of extreme fire weather expands, posing new challenges for fire management systems 12 , 31 . Looking ahead, tracing the spatio-temporal evolution of extreme FWI conditions and exploiting model large ensembles would help anticipate rare or unseen fire-weather events and strengthen European fire-response mechanisms in the face of climate change. Methods Data We used daily climate data from the period 1950–2024 from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis ERA5, providing data at a 0.25° spatial resolution 42 . Although ERA5 reanalysis data are available from 1940 onward, we chose to exclude the first decade as the limited number of assimilated observations during this period may introduce biases 61 . We used daily maximum temperature, daily sum of precipitation, daily minimum relative humidity, and daily mean 10 meters wind speed for the main analyses (we also tested the usage of other temperature and relative humidity values; see section Fire Weather Index ). The 10-meter wind speed was derived from the 10-meter u and v wind components at an hourly timestep, before averaging to daily resolution. In addition, we downloaded hourly temperature and hourly dew point temperature, which were used to derive daily minimum relative humidity based on the Python-based xclim climate indices library 48 , 62 , 63 . Solely for the analysis in Fig. 1 , ERA5 climate data were interpolated to the burned area (BA) grid for consistency; for all other analyses, we used the original ERA5 resolution. Both datasets were extracted for the spatial domain 22°W–45°E and 27–72°N, and a mask corresponding to the European countries under the European Protection Agreement was subsequently applied. Daily BA was retrieved from the Global Fire Emissions Database version 4.1 (GFEDv4.1), which covers the period 2001–2015 at a spatial resolution of 0.25 43 . E-OBS data for the four main meteorological variables were used to compare observational trends with those derived from ERA5 (see Supplementary Fig. 2). To analyse trends in climate variables and Fire Weather Index (FWI), we used simulations from 17 CMIP6 models 44 providing all required variables for computing FWI (see Supplementary Table 1). Historical simulations (1850–2014) were concatenated with data from the Shared Socioeconomic Pathway 3–7.0 (SSP3-7.0) scenario for 2015–2034 (the last 10 years being used to derive scaling factors via mobile window; see Methods section Detrending and scaling factors — CMIP6 Climate Models) . For each model, all available realizations meeting these criteria were retained, resulting in a pool of 163 ensemble members from 17 models (Supplementary Table 1). When deriving scaling factors from climate models, as a first step, data were regridded to the ERA5 grid using bilinear interpolation for temperature, relative humidity, and wind speed, and conservative remapping for precipitation. Fire Weather Index We computed the Canadian FWI using the Python-based xclim climate indices library 62 , based on the standard methodology of the Canadian Forest Fire Danger Rating System (CFFDRS) 64 . To account for the effect of inter-seasonal drought and derive more realistic FWI values at the early fire season, we applied the overwintering method to the drought code, following previous recommendations 65 . As the application of the overwintering requires the definition of a fire season, using burned area data from GFEDv4 and after summing from daily to monthly values, the fire season was selected as the shortest possible consecutive N-months that accounts for 90% of the average annual cumulative burned area, resulting in the 6-month period of May-October (Supplementary Fig. 1)––which is commonly considered the main fire season in Europe 66 . The Canadian FWI has been developed to represent the fire weather at its maximum afternoon peaks, and requires data of temperature, wind speed and relative humidity at noon local time, as well as 24-hour accumulated precipitation until noon 21 . However, the use of climate models—where mostly daily data are available—requires the use of daily approximations. Therefore, we used daily maximum temperature, daily sum of precipitation, mean daily wind speed and minimum daily relative humidity from CMIP6 climate models—and for consistency we used the same variables also for ERA5 reanalysis. Note that these proxies have been widely used in previous work to derive FWI from climate models 26 , 32 , 67 , 68 , and our ERA5-based tests show that the resulting values closely reproduce those obtained with local noon data (the local noon time was derived based on the longitude of each grid point 69 ). In particular, our chosen max-min proxies (listed above) perform better than using alternative daily mean proxies (that is, daily average of temperature and daily average of relative humidity) (Supplementary Fig. 7). Although a recent study has shown that using the max-min proxy rather than local noon data can lead to an overestimation of FWI trends, this overestimation is shown to be negligible in Europe and therefore does not represent a limitation for our analysis 70 . Relationship between BA extremes and FWI thresholds We assessed spatially compounding fire danger through the extent of European land exposed to FWI ≥ 50. While a FWI threshold of 50 is in line with the operational threshold used by the European Forest Fire Information System (EFFIS) to monitor ​​ extreme fire danger 24 , we further support this choice by assessing the relationship between extreme BA events and twelve FWI thresholds: six percentile-based (50, 60, 70, 80, 90, 95) and six absolute values (20, 30, 40, 50, 60, 70). For this assessment (Supplementary Fig. 8), we take two main steps. (1) For each FWI threshold, we computed the daily extent of European land exceeding that FWI threshold during the fire season; then, these daily extents are used to derive the associated empirical cumulative distribution function (ECDF). (2) For each FWI threshold, we averaged the ECDF values computed across the daily extents of European land associated with the top N = 10 days in terms of European-total BA. While our main focus is on the top N = 10 BA days, covering the most extreme European-total events, we also repeated this operation for N equal to 20, 30, 40, and 50, in order to explore the relationship for weaker European-total BA events. FWI thresholds that yield to a higher average ECDF values indicate stronger alignment between widespread extreme FWI conditions and European-total BA events. As a result, we find that for the top N = 10 European-total BA days, an absolute FWI threshold of 50 maximises the average ECDF values (reaching an average value of 0.89) across BA events, thus representing the most effective FWI threshold. This means that the extent of European land under FWI ≥ 50 on the top 10 European-total BA events is ranked, on average, among the top 11% of all days. Finally, we note that a threshold of 50 also performs well when considering different extremeness in European-total BA days (different N values) and that our approach shows that absolute FWI thresholds generally perform better than percentile-based ones at this continental scale during the fire season. Spatial correlations To quantify the contribution of cross-country correlations in FWI to spatially compounding fire danger, we compared the distribution of the daily European land area exposed to extreme FWI with the distribution obtained after breaking these correlations via shuffling data 71 (Fig. 2 ). For the shuffled data, we first computed each country’s daily time series of land area experiencing extreme FWI from the original data, and then removed cross-country, spatial correlations by randomly shuffling the time series of each country. We repeated this shuffling procedure 1,000 times to obtain an ensemble of shuffled realisations, and for each of them we computed the associated distribution of the daily European land area exposed to extreme FWI. To assess whether spatial correlations influence the distribution, we verified that the distribution derived from the original data lies outside the 95% confidence interval of the distribution obtained from the 1,000 shuffled realisations. The same methodology of removing cross-country correlations is applied to the BA data when assessing the effect of cross-country BA correlations on two BA-based metrics: (i) the number of countries experiencing at least 0.01% of their area burned on a given day, and (ii) the total daily burned area across Europe (Supplementary Fig. 3). Weather anomalies behind spatially compounding fire danger For each of the ten most extreme spatially compounding fire-danger events—selected among the annual maxima of the European land simultaneously exposed to extreme fire weather—we extracted daily standardized anomalies of FWI and the meteorological drivers of the FWI. For Temperature, Relative humidity, and Wind speed, we calculated the daily climatological mean and standard deviation by grouping the full time series by calendar day; standardized anomalies were then obtained by subtracting the daily climatological mean from the observed value, and dividing by the corresponding standard deviation. For Precipitation, standardized anomalies for a given day were computed similarly, but based on the cumulative precipitation over the preceding 30 days of the day of interest, as daily precipitation is highly variable. For each of the the top ten spatially compounding fire danger, standardized anomalies were extracted across all grid cells under extreme FWI conditions during the event window (covering the 80 days preceding and the 5 days following each event). Then for each day in the window, we computed the spatially weighted average (using grid cells area) of the anomalies across all grid cells under extreme FWI (Fig. 3 b-f). Detrending and scaling factors – ERA5 For each meteorological driver, we quantified how its trend contributed to the trend in spatially compounding fire danger. To do so, we detrended the meteorological driver, recomputed FWI while keeping the other drivers unchanged, and then we computed the contribution of each driver to the annual maxima extent of European land under extreme FWI as the difference between the annual maxima time series from the original and detrended data. Using the annual maximum—without constraining by the date of events between the original and detrended data—ensures that the most extreme events are captured in each simulation. In line with the so-called delta approach 55 – 57 , we detrended the daily field of temperature, relative humidity and wind, for each month M and year Y between 1950 and 2024, adjusting the field of the variable’s time series using an additive factor field equal to the difference between the mean over 3-month moving windows centered on the month M in the reference period (1950–1980) to the mean over 3-month moving windows centered on the month M over a 21 years window centered on the year Y. The resulting additive factor for the month M of the year Y was then applied uniformly to all days within that month of the year Y. We applied a similar method for detrending precipitation, but using a multiplicative factor field equal to the ratio of 3-month mean of precipitation in the reference period to the 3-month mean of precipitation over a 21 year window centered on the year Y. Note that for this ERA5-based detrending, the 21-years centered moving window is truncated at the boundaries of the time series as it cannot extend beyond the start or the end of the dataset (1950–2024). For the detrended relative humidity, a negligible fraction of values (0.02% of values among all grid points and days) fell outside physical bounds, that is below 0% or above 100%, and were therefore set to 0% and 100%, respectively, following previous work 72 . This detrending was applied to each of the four weather variables, and additionally to the case where all variables were detrended simultaneously. To assess the robustness of the detrending method, we confirmed that detrended FWI time series (obtained by directly detrending FWI through an additive factor) closely matches the FWI computed from detrended input variables (Supplementary Fig. 4), indicating that detrending all input variables provides a consistent representation of detrended FWI. To disentangle the contribution of temperature and specific humidity on relative humidity trends (Fig. 4 h), we applied the following decomposition method. This approach allows us to also assess the total contribution of temperature to FWI trends, defined as the combination of the temperature direct effect on FWI (given that temperature is one of the direct input variables of FWI) and the effect on FWI arising from temperature-driven changes in relative humidity (for fixed specific humidity). First, we computed specific humidity from ERA5 air temperature, dew point temperature and pressure, using the hourly data corresponding to the daily minimum relative humidity, individually for each grid point 62 . Then, we detrended specific humidity and temperature corresponding to the daily minimum of relative humidity using the additive method described earlier. Relative humidity is then recomputed using the Magnus equations, based on (i) detrended temperature (keeping specific humidity as observed) and (ii) detrended specific humidity (keeping temperature as observed). By averaging over Europe and during the fire season the difference between observed relative humidity and relative humidity from (i), we quantified the contribution of temperature-driven changes to long-term relative humidity trends 35 , 63 (Fig. 4 h, orange curve). Similarly, from the difference between observed relative humidity and relative humidity from (ii), we quantified the contribution of changes in specific humidity (Fig. 4 h, teal curve). A visual comparison shows that the sum of these two contributions (Fig. 4 h, purple curve) matches the long-term trend in relative humidity (Fig. 4 d, purple curve), confirming the robustness of the detrending approach. Detrending and scaling factors – CMIP6 climate models To quantify the human-induced climate change impact on recent spatially compounding fire danger events, we compared the annual-maximum extent of European land simultaneously experiencing extreme FWI under factual (original, observed ERA5-based) FWI against counterfactual FWI obtained after removing trends derived from CMIP6 climate models 44 (annual maxima are identified independently in factual and counterfactual data). In line with the so-called delta approach 55 – 57 , to derive the counterfactual FWI, for each ensemble member and for the four FWI meteorological driver, we derived monthly scaling factors (multiplicative for precipitation and additive for the other three FWI drivers) using the approach described above (see Methods, section Detrending and scaling factors – ERA5 ), taking preindustrial period 1850–1900 as the reference period. Then, the resulting monthly scaling factors were applied to ERA5 daily data to derive counterfactual meteorological drivers (as explained for ERA5 detrending in section above). Using these counterfactual meteorological drivers to recompute FWI allows for generating counterfactual FWI datasets in which long-term trends are removed from meteorological drivers. We then computed, for the scaling factors derived from each ensemble member of each model separately, the contribution of each driver to the annual-maximum extent of European land under extreme FWI as the difference between the annual maximum time series in factual against those of the counterfactual simulations. Such resulting annual differences derived from each model ensemble member were then averaged within each model, and finally combined into a multi-model mean. The above analysis was conducted in a case where all meteorological drivers were detrended simultaneously (for quantifying the total impact of climate change), and—to quantify individual driver contributions—in individual cases where only one meteorological driver at a time was detrended 23 , 50 ) (Fig. 5 ). To evaluate the ability of climate models in reproducing long-term trends in comparison with ERA5, we additionally recomputed scaling factors using 1950–1980 as reference period, and computed the linear trends in the resulting factors of each meteorological driver separately for the period 1960–2015 (Fig. 5 c and Supplementary Fig. 5). Using this period allows us to evaluate trends from the exact same data years in CMIP6 and ERA5 (a more extended period would not suffice, as scaling factors are derived based on 21-years moving windows and ERA5 is available for the period 1950–2024 only). Declarations Data availability ERA5 reanalysis data used in this study are publicly available from the Copernicus Climate Data Store ( https://cds.climate.copernicus.eu ). CMIP6 model outputs were obtained from the Earth System Grid Federation (ESGF) nodes ( https://esgf-metagrid.cloud.dkrz.de/search ). The GFEDv4 burned area product is publicly accessible at https://www.globalfiredata.org . E-OBS gridded observations are available from the European Climate Assessment and Dataset (ECA&D) project ( https://www.ecad.eu/download/ensembles/download.php ). Code availability All computer codes used for the analyses are available from the authors upon request. Acknowledgments We thank Yann Quilcaille for their initial input on the FWI calculation and for constructive feedback on the manuscript. The authors acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6, and the authors thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP and the ESGF. Analyses were carried out on the High-Performance Computing (HPC) Cluster EVE, a joint effort of both the Helmholtz Centre for Environmental Research – UFZ and the German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig. Funding Declaration This project received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via the Emmy Noether Programme (grant ID: 524780515). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003469. Author contributions EG and EB initiated the study and conceptualized the research; EB supervised the project; EG performed all analyses and created all figures; EG and EB wrote the paper, reviewed and edited the final manuscript. Competing Interests The authors declare no competing interests. References Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4, 1321–1326 (2020). 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Supplementary Files SupplementarymaterialSpatiallycompoundingfiredangerGauthierBevacqua.pdf Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2026 Read the published version in npj Natural Hazards → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 27 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2001-2015, categorized by the \\u003c/em\\u003eextent\\u003cem\\u003e of European land area (%) exposed to FWI ≥ 50.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/61a5dafdd4cfd0b128c050a5.png\"},{\"id\":100595330,\"identity\":\"10444d37-b1d5-4751-bd41-e533b1bf2a7d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-19 13:48:14\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":364832,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eImpact of cross-country correlations on spatially compounding fire danger over Europe.\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Distribution of the percentage of European land simultaneously exposed to extreme fire weather (FWI ≥ 50) during the fire season in observations (green) and in shuffled data where cross-country FWI correlations are removed (black; see Methods). For the shuffled data, dots and shading indicate the mean and the 95% confidence interval from 1,000 random spatial shuffles. The difference between observed and shuffled-based distributions quantifies the contribution of spatial, cross-country correlations to spatially compounding fire danger. The inset highlights the upper tail.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/542a3d3ed0d259d8f18bc096.png\"},{\"id\":100569721,\"identity\":\"ab3f4ee3-cb80-4e7b-a5e2-fd6972597feb\",\"added_by\":\"auto\",\"created_at\":\"2026-01-19 09:26:27\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":953136,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eSpatial distribution and weather drivers of extreme spatially compounding fire danger. a\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Number of times each location was impacted by one of the ten most extreme spatially compounding fire danger events, with the events selected among the annual-maximum extents of the European land simultaneously exposed to extreme fire weather (FWI ≥ 50). \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003eb\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Time evolution of standardized FWI anomaliesaveraged over locations affected by top ten spatially compounding fire danger events, spanning 80 days before to 5 days after the event (day 0 marks the date of the event). Light grey lines show individual events, while colored bold lines indicate the multi-event mean. \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003ec-f \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;Same as panel b, but for Temperature (d), Precipitation (e), Relative humidity (RH) (e), and wind speed (f). Precipitation is expressed as the anomaly in cumulative precipitation over the 30 days preceding each date shown.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/7dbcc16be6aa56edba1a91d0.png\"},{\"id\":100595051,\"identity\":\"3aa5276c-ad60-4400-a65f-810b50d72f98\",\"added_by\":\"auto\",\"created_at\":\"2026-01-19 13:47:14\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":539315,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eObserved long-term trend in extreme fire weather and its drivers in Europe\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e. \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003ea \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003eTime series of the Fire Weather Index (FWI) averaged over Europe during the fire season (May–October). \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003eb-e\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003eSame as panel a, but for FWI meteorological drivers: daily maximum temperature (b), mean monthly precipitation (c), daily minimum relative humidity (RH) (d), and mean daily wind speed (e). \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003ef\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Annual-maximum extent of European land experiencing extreme fire weather (FWI ≥ 50). \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003eg\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003eContributions of observed trends in individual meteorological drivers to the observed trends in the spatial extent of extreme fire danger events, quantified by recomputing FWI after detrending each driver separately. The left y-axis shows the absolute contributions in the extent (%) of European land affected, while the right y-axis shows the contributions in km². \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003eh\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Decomposition of relative humidity trends into contributions from temperature trends (orange) and specific humidity trends (teal), averaged over Europe during the fire season (May-October). The purple curve shows the total contribution, calculated as the sum of the temperature-driven and specific humidity-driven contributions.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/275923923a7f817233c26bc5.png\"},{\"id\":100595600,\"identity\":\"5527e90e-efce-4ceb-8143-7a03c3062827\",\"added_by\":\"auto\",\"created_at\":\"2026-01-19 13:48:53\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":890353,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eEffect of human-induced climate change on spatially compounding fire danger over Europe.\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003ea\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Contribution of climate change (%), arising from changes (relative to 1850–1900) in different meteorological drivers, to the annual-maximum extent of European land under fire weather extremes (averaged across annual maxima over 2014-2024). Bars show the multimodel mean across 17 CMIP6 climate models, black whiskers indicate the interquartile range, and grey dots represent individual model values outside that range (one value per model). \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003eb\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Same as panel a, but for linear trend of these climate change contributions over 1994–2024 (see Methods). \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003ec\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003eTemperature change relative to 1950-1980 for ERA5 (black) and all ensemble members for CMIP6 models (in colors). Each color is associated with an individual model. \\u003c/em\\u003e\\u003cem\\u003e\\u003cstrong\\u003ed\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cem\\u003e Annual climate change contribution (%) since 1850–1900 from temperature trends to the extent of European land under extreme fire danger conditions. The legend shows the datasets used, with the number of ensemble members indicated in parentheses.\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/927508c8ea780dac36a497ac.png\"},{\"id\":107552684,\"identity\":\"42d5a44e-65ea-4727-b8b8-af62170b0c8f\",\"added_by\":\"auto\",\"created_at\":\"2026-04-22 14:26:39\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2846262,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/b8257170-c2dd-4af3-acfe-cb4635606b78.pdf\"},{\"id\":100569720,\"identity\":\"ac59bc76-da1f-4d9f-aa4d-3156cbbbe842\",\"added_by\":\"auto\",\"created_at\":\"2026-01-19 09:26:27\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3147925,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementarymaterialSpatiallycompoundingfiredangerGauthierBevacqua.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8583363/v1/96a6b85946312e316c9aefe5.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Human-induced climate change intensifies spatially compounding fire weather extremes across European countries\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eWildfires are a natural component of many vegetated ecosystems, yet they also rank among the most destructive climate-related hazards\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. In recent years, the European Union (EU) has seen a substantial rise in burned area\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. Indeed, 2025 has been the worst year on record, with widespread wildfires and over 1\\u0026nbsp;million hectares burned\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. To respond more effectively to disasters such as wildfires, 37 countries cooperate through the EU Civil Protection Mechanism, which enables the sharing of firefighting aircraft, personnel, and medical support\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. However, when spatially compounding fires\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e occur\\u0026ndash;\\u0026ndash;events that simultaneously affect multiple regions\\u0026mdash;the accumulated damage can be substantial, and the shared capacities of the mechanism can be overloaded, limiting response action and potentially enhancing impacts\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR11\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. For example, in 2021, fires occurred in 22 European countries, creating unprecedented strain on firefighting services\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e. In 2025 alone, the mechanism was activated 18 times in 11 countries, deploying 58 aerial firefighting means\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e. Wildfires accounted for nearly 20% of all assistance requests to the EU mechanism between 2007 and 2024\\u003csup\\u003e14\\u003c/sup\\u003e, underscoring the need to gain a better understanding of spatially compounding fire danger events.\\u003c/p\\u003e \\u003cp\\u003eGiven fuel and ignition, compound hot, dry and windy weather conditions strongly influence fire occurrence, behaviour and impacts\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. Such weather conditions are integrated into the concept of fire weather, a term encompassing key meteorological drivers like temperature, relative humidity, wind, and precipitation\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. Fire weather controls the timing and spread of fires, and also regulates much of the interannual variability in burned area\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e. While various indices have been developed, one of the most widely used is the \\u003cem\\u003eCanadian Forest Fire Weather Index\\u003c/em\\u003e (hereinafter FWI), which integrates key meteorological drivers into six subcomponents representing fuel dryness, fire spread, and ignition potential\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. The FWI correlates well with observed burned area over Europe\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e and is used operationally by the European Forest Fire Information System (EFFIS) to monitor fire danger\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e, making it a relevant index for analysing spatially compounding fire danger. Across the continent, climate change is projected to intensify FWI extremes, particularly in southern Europe\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e, with rising temperatures and decreasing relative humidity contributing to longer fire seasons and more frequent FWI extreme events\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34\\\" citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eDespite the intensification of FWI extremes, little is known about spatially compounding fire danger in Europe, defined as days when a large extent of European land experiences extreme FWI conditions simultaneously. This gap arises as fire danger has traditionally been assessed at individual locations, without considering the spatial interplay or correlations in FWI across regions\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. For the United States, Abatzoglou et al. (2021) showed that days of synchronous fire danger closely align with periods of intense strain on national fire management resources\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. Similarly, Richardson et al. (2025) showed the potential for increased challenges in firefighting cooperation across the United States, Canada and Australia\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. Torres-V\\u0026aacute;zquez et al. (2025) analysed historical trends, and future projections of synchronous FWI extremes for individual European IPCC regions under synthetic, spatially uniform temperature and precipitation trends, identifying Central Europe as a hotspot of increasing synchronicity\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e. However, their framework\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e does not explicitly account for relative humidity, thereby neglecting the indirect effects of temperature on atmospheric dryness through changes in moisture-holding capacity. Overall, the contribution of trends in multiple FWI drivers to spatially compounding fire danger remains unclear. This includes the role of temperature trends and its impact on relative humidity, the physical drivers of individual extreme compounding events, and the influence of human-induced climate change on recent trends. Moreover, while correlations among compounding drivers are known to affect risk\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR40\\\" citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026mdash;meaning that cross-country FWI correlations may increase or decrease the likelihood of widespread fire danger events across Europe\\u0026mdash;the impact of such spatial correlations remains unexplored.\\u003c/p\\u003e \\u003cp\\u003eTo provide a systematic assessment of spatially compounding fire in Europe, we investigate extreme fire danger across countries participating in the EU Civil Protection framework (hereinafter referred to as Europe). Using the ERA5 climate reanalysis\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e, we calculate the FWI for the fire season (May to October, see Supplementary Fig.\\u0026nbsp;1). We then combine it with observed burned area (BA) from the GFEDv4.1 Database\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e to inspect the relationship between historical BA events and FWI conditions, and then analyse the effects that spatial correlations have on spatially extended FWI extremes. An event-based approach is employed to investigate the key meteorological anomalies underlying extreme spatially compounding fire danger events. To assess how climate change has shaped spatially compounding fire danger in recent decades, we quantify long-term trends in the FWI and isolate the contributions to these trends from key meteorological variables by using ERA5 and climate models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6)\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEuropean burned area rises with the spatial extent of FWI extremes\\u003c/h2\\u003e \\u003cp\\u003eThe Fire Weather Index (FWI) is a cornerstone fire danger indicator in Europe, and the European Forest Fire Information System (EFFIS) operationally defines extreme fire danger when the FWI exceeds 50\\u003csup\\u003e24\\u003c/sup\\u003e. Building on this operational definition of an absolute FWI threshold\\u0026mdash;which we further supported by exploring the BA-FWI relationship across multiple FWI thresholds (see Methods)\\u0026mdash;we employ the daily extent of European land experiencing FWI\\u0026thinsp;\\u0026ge;\\u0026thinsp;50 simultaneously for characterising the intensity of spatially compounding fire danger. By combining this metric with daily BA data, we find that the more widespread the spatially compounding fire danger, the larger is the European-total BA. Specifically, by grouping daily European-total BA by the extent of land under FWI\\u0026thinsp;\\u0026ge;\\u0026thinsp;50, we observe a clear shift of the BA distribution toward higher values as the extent increases (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In other words, the larger the land simultaneously experiencing extreme fire weather, the more likely Europe is to record very high BA totals. Although wildfire occurrence also depends on ignition sources and fuel availability, these results indicate that FWI provides valuable insight into spatially compounding fire danger, which is consistent with earlier studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eCross-country spatial correlations of extreme fire weather enhance spatially compounding fire danger\\u003c/h3\\u003e\\n\\u003cp\\u003eTo quantify the influence of cross-country correlations in FWI on spatially compounding fire danger, we compare the distribution of the European land simultaneously experiencing extreme FWI in observed versus spatially shuffled data during the fire season (see Methods) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Our results reveal that cross-country correlations in FWI substantially amplify extreme events characterized by a large extent of European land simultaneously experiencing extreme FWI. For example, the likelihood of a day when at least 13% of European land is under extreme FWI is 56 times higher than expected in the absence of spatial correlations during the fire season. Overall, these results demonstrate that, due to spatial correlations in FWI, multiple European countries tend to experience extreme FWI simultaneously, which can enhance the potential for widespread fires.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eHaving established that spatial correlations amplify the probability of widespread extreme fire weather, we now investigate the local-scale atmospheric anomalies that trigger the most extreme widespread events. To do so, we focus on the ten days between 1950 and 2024 with the largest annual-maximum extent of European land experiencing extreme FWI, which mainly affected southern and central Europe (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). Extreme FWI rarely occur due to short-term weather anomalies in isolation, but rather emerge from a progressive build-up of meteorological anomalies\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. On average, positive FWI anomalies emerge up to about two months before the event and typically intensify markedly around 10 days before the event (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb), closely tracking a similar rise in temperature anomalies (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec). Negative anomalies in precipitation and relative humidity dominate the pre-event period, with relative humidity reaching its minimum on the day of the extreme, consistent with its drying effect on fuel moisture\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. Wind speed anomalies are less systematic, though positive anomalies are observed on the event day, in line with wind\\u0026rsquo;s effect on the pace of fire spread\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. Altogether, these patterns provide new insights into the evolution of local extreme fire weather during spatially compounding events in Europe, highlighting the role of past weather anomalies in building up extreme fire danger.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eTemperature and relative humidity as main drivers of observed long-term trends synchronous fire weather\\u003c/h3\\u003e\\n\\u003cp\\u003eExpanding the scope beyond single events to better understand the long-term evolution of spatially compounding fire danger and its underlying drivers, we first analysed trends in FWI and its key meteorological drivers averaged across Europe during the fire season using ERA5 reanalysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea-e). Averaged daily FWI shows relatively high values in the 1950s\\u0026ndash;1960s, followed by a decrease until around 1970, and a renewed increase in recent decades. Among FWI drivers, the annual averaged daily maximum temperature exhibits a pronounced upward trend since the 1980s, in line with the intensification of heat extremes across Europe\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. Daily minimum relative humidity displays an increase until about 1980, followed by a persistent decline thereafter. Mean monthly precipitation increased slightly during 1950\\u0026ndash;1970 but has remained relatively stable since, while wind speed shows no clear long-term trend. ERA5-based trends are consistent with independent observational datasets such as the Ensemble of daily precipitation and temperature records for Europe (E-OBS) (Supplementary Fig.\\u0026nbsp;2) and corroborate previous findings from the literature\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e, strengthening confidence in the observed large-scale weather changes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo assess spatially compounding fire danger, we inspected the annual-maximum extent of the European land simultaneously exposed to extreme FWI (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ef). In line with the FWI averaged across Europe (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea), this spatial extent was relatively high in the 1950s, declined in the 1960s\\u0026ndash;1970s, and increased again from the 1980s onward. While the long-term evolution of the mean weather drivers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb-e) offers an initial indication of their relative influence on spatially compounding fire danger trends, quantifying the individual effect of trends in weather drivers requires further analyses. To this end, we detrended each FWI input variable separately and recomputed the FWI (see Methods). Comparing the annual-maximum extent of European land under extreme fire danger between the original and detrended simulations allows to isolate the contribution of each driver (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eg; note that the annual maxima are identified independently in each simulation and can therefore occur on different calendar days across simulations). We find that relative humidity largely explains the temporal evolution of the spatial extent of FWI extremes in the mid-20th century, whereas in recent decades increasing temperatures and decreasing relative humidity expanded the area affected by FWI extremes.\\u003c/p\\u003e \\u003cp\\u003eWhile the decomposition reveals that relative humidity and temperature are the main contributors to FWI trends over Europe, these two variables are physically linked, as relative humidity represents the ratio of actual specific humidity to saturation specific humidity\\u0026mdash;the latter being primarily controlled by temperature (as saturation specific humidity increases with temperature) and, to a lesser extent, by pressure\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e. This implies that higher temperature reduces relative humidity\\u0026mdash;provided that specific humidity does not increase at the same rate. Thus, following previous work\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e, we detrended relative humidity to disentangle the contributions of temperature and specific humidity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eh). Specific humidity has increased since 1950, leading to a rise in relative humidity during the mid-20th century as this increase was not yet counteracted by the increasing temperature. After around 1980, however, this increase has been outweighed by the strong warming, which, by increasing saturation specific humidity, led to a decline in relative humidity. Such a decomposition further allows for quantifying the overall contribution of temperature trends to FWI extremes, after accounting for both its direct effect and indirect effect via relative humidity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eg, orange dashed curve). This demonstrates that rising temperature is a key driver of the recent increase in the spatial extent of extreme fire danger events across Europe. Overall, these findings highlight that the observed increases in the extent of widespread fire danger events across Europe are largely driven by rising temperatures and decreasing relative humidity.\\u003c/p\\u003e\\n\\u003ch3\\u003eHuman-induced climate change amplifies synchronicity in wildfire-prone weather\\u003c/h3\\u003e\\n\\u003cp\\u003eThe detected observed trends alone cannot inform on the total contribution of human-induced climate change, which requires information on the pre-industrial climate. Furthermore, internal climate variability can substantially influence observed trends, challenging the isolation of human-induced climate change from observed trends\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e. We therefore used CMIP6 models to detrend each FWI input variable based on a pre-industrial reference period to assess the total human-induced climate change effect\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e (see Methods). Based on the multimodel mean \\u0026mdash; which reduces noise from internal variability and averages forced signals across models\\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e \\u0026mdash; we estimate that human-induced climate change contributed, on average over the last decade, to 14.8% (4.8\\u0026ndash;25.6%, interquartile range across models) of the annual-maximum land surface synchronously affected by extreme FWI. About 76% (13 out of 17) of the models agree on the positive contribution (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea). This corresponds to an increase of nearly 11 900 000 square kilometers of land surface (1.9% of the European land) synchronously affected by extreme FWI. In line with ongoing warming over Europe (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec), temperature was the dominant driver of this human-induced climate change effect, with all climate models (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea) and all 163 ensemble members (not shown) agreeing on its positive influence (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ed). Precipitation trends also slightly enhanced spatially compounding fire danger, whereas relative humidity effects\\u0026mdash;although positive contributions being stronger than those from precipitation\\u0026mdash;are more uncertain. In contrast, the influence of wind is estimated to be negligible. Note that the magnitude of the warming influence varies over time consistently across model ensemble members (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ed), in line with a non-linear response of spatially compounding fire danger to warming.\\u003c/p\\u003e \\u003cp\\u003eOverall, even more robust patterns emerge when examining the temporal trend of these contributions during 1994\\u0026ndash;2024 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eb), with a human-induced climate change increase in spatially compounding fire danger of 0.47% per year and 90% (15 out of 17) of the models agreeing on the positive trend. Also, for contributions to these trends, temperature and relative humidity consistently emerge as the main drivers. Finally, we note that the model spread (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea,b) reflects both internal climate variability and model differences in the representation of the forced signal.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eRecent decades have seen a rise in large-scale wildfires across Europe\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e, posing growing challenges to shared-response mechanisms such as the European Civil Protection Agreement\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e. By combining burned area data, climate reanalyses, and CMIP6 model simulations, our study shows that days of highest total burned area over Europe are associated with widespread extreme fire weather conditions. Over the last three decades the annual-maximum extent of European land simultaneously exposed to extreme fire weather has increased markedly, indicating a growing concurrent exposure of European countries to fire-conducive weather. While anomalies in multiple weather variables contribute to individual extreme events, temperature and relative humidity stands as the primary drivers of historical trends in spatially compounding fire danger. In addition to directly increasing FWI, rising temperatures have enhanced the atmosphere\\u0026rsquo;s moisture-holding capacity, thereby favouring a decline in relative humidity, which in turn has led to higher FWI\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e. Consistent with these trends, human-induced climate change has contributed, on average over the last decade, to about 15% of the annual-maximum extent of the European land synchronously experiencing extreme FWI conditions. Our findings on spatially compounding fire danger are strengthened by previous studies focussing on fire danger at individual locations, which also identified temperature and relative humidity as the key drivers of increasing FWI\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe fact that the spatial extent of extreme FWI conditions is enhanced by cross-country FWI correlations indicates that large-scale atmospheric conditions play a key role in shaping spatially compounding fire danger. Since extreme FWI occur under high temperature and low relative humidity anomalies\\u0026mdash;with these anomalies being typically linked to persistent anticyclonic circulation\\u0026mdash;our results suggest that widespread FWI events are associated with such large-scale, high-pressure regimes\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e, including atmospheric blocking and ridging\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e. However, we also note that the effects of cross-country spatial correlations on European-total burned area are less pronounced, although cross-country correlations affect the number of countries having simultaneously at least 0.01% of their territory burned (Supplementary Fig.\\u0026nbsp;3). The difference in cross-country correlations effects between FWI and burned area aligns with the fact that FWI extremes favour fires, but wildfire events are also strongly modulated by human factors (such as ignition patterns and suppression practices), and availability of fuels\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. Nevertheless, cross-country correlations in FWI imply that multiple countries often experience extreme fire weather synchronously, indicating the relevance of accounting for spatial dependencies in fire danger assessments and the design of pan-European early-warning systems\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn line with the so-called delta approach\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR56\\\" citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e, to quantify the contribution of climate variables trends to spatially compounding fire danger, we detrended individually each meteorological input. While different approaches exist for creating counterfactuals, each with its own advantages and limitations\\u003csup\\u003e\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u003c/sup\\u003e, the time series of FWI computed from detrended meteorological drivers shows strong agreement with the detrended FWI, supporting our detrending approach (Supplementary Fig.\\u0026nbsp;4). The effectiveness of our detrending of FWI based on season-specific three-month moving windows to study FWI long-term trends aligns with the fact that FWI is largely influenced by weather anomalies over months (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), due to the well-known memory of the index\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. However, we also note that the influence of wind trends should be interpreted with caution, as wind mostly modulates FWI at short timescales. While using climate models is key for attribution studies, models may have biases\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u003c/sup\\u003e and we find that while CMIP6 models capture European temperature and precipitation trends\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e, they underestimate the observed decline in relative humidity\\u003csup\\u003e\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e\\u003c/sup\\u003e (and tend to overestimate decreasing wind speed\\u003csup\\u003e\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u003c/sup\\u003e) (Supplementary Fig.\\u0026nbsp;5). Such an underestimation of the relative humidity decline implies that our estimated amplification of spatially compounding fire danger from human-induced climate change may be conservative. This aspect is particularly relevant as the decline in relative humidity is strongest across southern and central Europe, where it directly influences spatially compounding fire danger (Supplementary Fig.\\u0026nbsp;6). Reinforcing the conservative interpretation, the only four out of seventeen models that indicate a reduction in spatially compounding fire danger under climate change (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea) are among those that miss most the observed decline in relative humidity.\\u003c/p\\u003e \\u003cp\\u003eOur findings demonstrate the growing influence of human-induced warming on widespread spatially compounding fire danger events, primarily due to warming and declining relative humidity. Combined with projections of continued warming that will further intensify extreme fire weather\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e, our results indicate that these spatially compounding events may expand further in the future. Regions such as Western and Central Europe, historically less affected by extreme FWI conditions, may be drawn more frequently into large-scale events as the spatial extent of extreme fire weather expands, posing new challenges for fire management systems\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e. Looking ahead, tracing the spatio-temporal evolution of extreme FWI conditions and exploiting model large ensembles would help anticipate rare or unseen fire-weather events and strengthen European fire-response mechanisms in the face of climate change.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eData\\u003c/h2\\u003e \\u003cp\\u003eWe used daily climate data from the period 1950\\u0026ndash;2024 from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis ERA5, providing data at a 0.25\\u0026deg; spatial resolution\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e. Although ERA5 reanalysis data are available from 1940 onward, we chose to exclude the first decade as the limited number of assimilated observations during this period may introduce biases\\u003csup\\u003e\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e. We used daily maximum temperature, daily sum of precipitation, daily minimum relative humidity, and daily mean 10 meters wind speed for the main analyses (we also tested the usage of other temperature and relative humidity values; see section \\u003cem\\u003eFire Weather Index\\u003c/em\\u003e). The 10-meter wind speed was derived from the 10-meter u and v wind components at an hourly timestep, before averaging to daily resolution. In addition, we downloaded hourly temperature and hourly dew point temperature, which were used to derive daily minimum relative humidity based on the Python-based xclim climate indices library\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e. Solely for the analysis in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, ERA5 climate data were interpolated to the burned area (BA) grid for consistency; for all other analyses, we used the original ERA5 resolution. Both datasets were extracted for the spatial domain 22\\u0026deg;W\\u0026ndash;45\\u0026deg;E and 27\\u0026ndash;72\\u0026deg;N, and a mask corresponding to the European countries under the European Protection Agreement was subsequently applied. Daily BA was retrieved from the Global Fire Emissions Database version 4.1 (GFEDv4.1), which covers the period 2001\\u0026ndash;2015 at a spatial resolution of 0.25\\u003csup\\u003e43\\u003c/sup\\u003e. E-OBS data for the four main meteorological variables were used to compare observational trends with those derived from ERA5 (see Supplementary Fig.\\u0026nbsp;2).\\u003c/p\\u003e \\u003cp\\u003eTo analyse trends in climate variables and Fire Weather Index (FWI), we used simulations from 17 CMIP6 models\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e providing all required variables for computing FWI (see Supplementary Table\\u0026nbsp;1). Historical simulations (1850\\u0026ndash;2014) were concatenated with data from the Shared Socioeconomic Pathway 3\\u0026ndash;7.0 (SSP3-7.0) scenario for 2015\\u0026ndash;2034 (the last 10 years being used to derive scaling factors via mobile window; see Methods section \\u003cem\\u003eDetrending and scaling factors \\u0026mdash; CMIP6 Climate Models)\\u003c/em\\u003e. For each model, all available realizations meeting these criteria were retained, resulting in a pool of 163 ensemble members from 17 models (Supplementary Table\\u0026nbsp;1). When deriving scaling factors from climate models, as a first step, data were regridded to the ERA5 grid using bilinear interpolation for temperature, relative humidity, and wind speed, and conservative remapping for precipitation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eFire Weather Index\\u003c/h3\\u003e\\n\\u003cp\\u003eWe computed the Canadian FWI using the Python-based xclim climate indices library\\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e, based on the standard methodology of the Canadian Forest Fire Danger Rating System (CFFDRS)\\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e. To account for the effect of inter-seasonal drought and derive more realistic FWI values at the early fire season, we applied the overwintering method to the drought code, following previous recommendations\\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e. As the application of the overwintering requires the definition of a fire season, using burned area data from GFEDv4 and after summing from daily to monthly values, the fire season was selected as the shortest possible consecutive N-months that accounts for 90% of the average annual cumulative burned area, resulting in the 6-month period of May-October (Supplementary Fig.\\u0026nbsp;1)\\u0026ndash;\\u0026ndash;which is commonly considered the main fire season in Europe\\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe Canadian FWI has been developed to represent the fire weather at its maximum afternoon peaks, and requires data of temperature, wind speed and relative humidity at noon local time, as well as 24-hour accumulated precipitation until noon\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. However, the use of climate models\\u0026mdash;where mostly daily data are available\\u0026mdash;requires the use of daily approximations. Therefore, we used daily maximum temperature, daily sum of precipitation, mean daily wind speed and minimum daily relative humidity from CMIP6 climate models\\u0026mdash;and for consistency we used the same variables also for ERA5 reanalysis. Note that these proxies have been widely used in previous work to derive FWI from climate models\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e\\u003c/sup\\u003e, and our ERA5-based tests show that the resulting values closely reproduce those obtained with local noon data (the \\u003cem\\u003elocal noon\\u003c/em\\u003e time was derived based on the longitude of each grid point\\u003csup\\u003e\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u003c/sup\\u003e). In particular, our chosen \\u003cem\\u003emax-min proxies\\u003c/em\\u003e (listed above) perform better than using alternative \\u003cem\\u003edaily mean proxies\\u003c/em\\u003e (that is, daily average of temperature and daily average of relative humidity) (Supplementary Fig.\\u0026nbsp;7). Although a recent study has shown that using the \\u003cem\\u003emax-min\\u003c/em\\u003e proxy rather than local noon data can lead to an overestimation of FWI trends, this overestimation is shown to be negligible in Europe and therefore does not represent a limitation for our analysis\\u003csup\\u003e\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRelationship between BA extremes and FWI thresholds\\u003c/h2\\u003e \\u003cp\\u003eWe assessed spatially compounding fire danger through the extent of European land exposed to FWI\\u0026thinsp;\\u0026ge;\\u0026thinsp;50. While a FWI threshold of 50 is in line with the operational threshold used by the European Forest Fire Information System (EFFIS) to monitor ​​\\u003cem\\u003eextreme fire danger\\u003c/em\\u003e\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e, we further support this choice by assessing the relationship between extreme BA events and twelve FWI thresholds: six percentile-based (50, 60, 70, 80, 90, 95) and six absolute values (20, 30, 40, 50, 60, 70). For this assessment (Supplementary Fig.\\u0026nbsp;8), we take two main steps. (1) For each FWI threshold, we computed the daily extent of European land exceeding that FWI threshold during the fire season; then, these daily extents are used to derive the associated empirical cumulative distribution function (ECDF). (2) For each FWI threshold, we averaged the ECDF values computed across the daily extents of European land associated with the top N\\u0026thinsp;=\\u0026thinsp;10 days in terms of European-total BA. While our main focus is on the top N\\u0026thinsp;=\\u0026thinsp;10 BA days, covering the most extreme European-total events, we also repeated this operation for N equal to 20, 30, 40, and 50, in order to explore the relationship for weaker European-total BA events. FWI thresholds that yield to a higher average ECDF values indicate stronger alignment between widespread extreme FWI conditions and European-total BA events. As a result, we find that for the top N\\u0026thinsp;=\\u0026thinsp;10 European-total BA days, an absolute FWI threshold of 50 maximises the average ECDF values (reaching an average value of 0.89) across BA events, thus representing the most effective FWI threshold. This means that the extent of European land under FWI\\u0026thinsp;\\u0026ge;\\u0026thinsp;50 on the top 10 European-total BA events is ranked, on average, among the top 11% of all days. Finally, we note that a threshold of 50 also performs well when considering different extremeness in European-total BA days (different N values) and that our approach shows that absolute FWI thresholds generally perform better than percentile-based ones at this continental scale during the fire season.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpatial correlations\\u003c/h2\\u003e \\u003cp\\u003eTo quantify the contribution of cross-country correlations in FWI to spatially compounding fire danger, we compared the distribution of the daily European land area exposed to extreme FWI with the distribution obtained after breaking these correlations via shuffling data\\u003csup\\u003e\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). For the shuffled data, we first computed each country\\u0026rsquo;s daily time series of land area experiencing extreme FWI from the original data, and then removed cross-country, spatial correlations by randomly shuffling the time series of each country. We repeated this shuffling procedure 1,000 times to obtain an ensemble of shuffled realisations, and for each of them we computed the associated distribution of the daily European land area exposed to extreme FWI. To assess whether spatial correlations influence the distribution, we verified that the distribution derived from the original data lies outside the 95% confidence interval of the distribution obtained from the 1,000 shuffled realisations. The same methodology of removing cross-country correlations is applied to the BA data when assessing the effect of cross-country BA correlations on two BA-based metrics: (i) the number of countries experiencing at least 0.01% of their area burned on a given day, and (ii) the total daily burned area across Europe (Supplementary Fig.\\u0026nbsp;3).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eWeather anomalies behind spatially compounding fire danger\\u003c/h2\\u003e \\u003cp\\u003eFor each of the ten most extreme spatially compounding fire-danger events\\u0026mdash;selected among the annual maxima of the European land simultaneously exposed to extreme fire weather\\u0026mdash;we extracted daily standardized anomalies of FWI and the meteorological drivers of the FWI. For Temperature, Relative humidity, and Wind speed, we calculated the daily climatological mean and standard deviation by grouping the full time series by calendar day; standardized anomalies were then obtained by subtracting the daily climatological mean from the observed value, and dividing by the corresponding standard deviation. For Precipitation, standardized anomalies for a given day were computed similarly, but based on the cumulative precipitation over the preceding 30 days of the day of interest, as daily precipitation is highly variable. For each of the the top ten spatially compounding fire danger, standardized anomalies were extracted across all grid cells under extreme FWI conditions during the event window (covering the 80 days preceding and the 5 days following each event). Then for each day in the window, we computed the spatially weighted average (using grid cells area) of the anomalies across all grid cells under extreme FWI (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb-f).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDetrending and scaling factors \\u0026ndash; ERA5\\u003c/h2\\u003e \\u003cp\\u003eFor each meteorological driver, we quantified how its trend contributed to the trend in spatially compounding fire danger. To do so, we detrended the meteorological driver, recomputed FWI while keeping the other drivers unchanged, and then we computed the contribution of each driver to the annual maxima extent of European land under extreme FWI as the difference between the annual maxima time series from the original and detrended data. Using the annual maximum\\u0026mdash;without constraining by the date of events between the original and detrended data\\u0026mdash;ensures that the most extreme events are captured in each simulation. In line with the so-called delta approach\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR56\\\" citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e, we detrended the daily field of temperature, relative humidity and wind, for each month M and year Y between 1950 and 2024, adjusting the field of the variable\\u0026rsquo;s time series using an additive factor field equal to the difference between the mean over 3-month moving windows centered on the month M in the reference period (1950\\u0026ndash;1980) to the mean over 3-month moving windows centered on the month M over a 21 years window centered on the year Y. The resulting additive factor for the month M of the year Y was then applied uniformly to all days within that month of the year Y. We applied a similar method for detrending precipitation, but using a multiplicative factor field equal to the ratio of 3-month mean of precipitation in the reference period to the 3-month mean of precipitation over a 21 year window centered on the year Y. Note that for this ERA5-based detrending, the 21-years centered moving window is truncated at the boundaries of the time series as it cannot extend beyond the start or the end of the dataset (1950\\u0026ndash;2024). For the detrended relative humidity, a negligible fraction of values (0.02% of values among all grid points and days) fell outside physical bounds, that is below 0% or above 100%, and were therefore set to 0% and 100%, respectively, following previous work\\u003csup\\u003e\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThis detrending was applied to each of the four weather variables, and additionally to the case where all variables were detrended simultaneously. To assess the robustness of the detrending method, we confirmed that detrended FWI time series (obtained by directly detrending FWI through an additive factor) closely matches the FWI computed from detrended input variables (Supplementary Fig.\\u0026nbsp;4), indicating that detrending all input variables provides a consistent representation of detrended FWI.\\u003c/p\\u003e \\u003cp\\u003eTo disentangle the contribution of temperature and specific humidity on relative humidity trends (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eh), we applied the following decomposition method. This approach allows us to also assess the total contribution of temperature to FWI trends, defined as the combination of the temperature direct effect on FWI (given that temperature is one of the direct input variables of FWI) and the effect on FWI arising from temperature-driven changes in relative humidity (for fixed specific humidity). First, we computed specific humidity from ERA5 air temperature, dew point temperature and pressure, using the hourly data corresponding to the daily minimum relative humidity, individually for each grid point\\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e. Then, we detrended specific humidity and temperature corresponding to the daily minimum of relative humidity using the additive method described earlier. Relative humidity is then recomputed using the Magnus equations, based on (i) detrended temperature (keeping specific humidity as observed) and (ii) detrended specific humidity (keeping temperature as observed). By averaging over Europe and during the fire season the difference between observed relative humidity and relative humidity from (i), we quantified the contribution of temperature-driven changes to long-term relative humidity trends\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eh, orange curve). Similarly, from the difference between observed relative humidity and relative humidity from (ii), we quantified the contribution of changes in specific humidity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eh, teal curve). A visual comparison shows that the sum of these two contributions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eh, purple curve) matches the long-term trend in relative humidity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed, purple curve), confirming the robustness of the detrending approach.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDetrending and scaling factors \\u0026ndash; CMIP6 climate models\\u003c/h2\\u003e \\u003cp\\u003eTo quantify the human-induced climate change impact on recent spatially compounding fire danger events, we compared the annual-maximum extent of European land simultaneously experiencing extreme FWI under factual (original, observed ERA5-based) FWI against counterfactual FWI obtained after removing trends derived from CMIP6 climate models\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e (annual maxima are identified independently in factual and counterfactual data). In line with the so-called delta approach\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR56\\\" citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e, to derive the counterfactual FWI, for each ensemble member and for the four FWI meteorological driver, we derived monthly scaling factors (multiplicative for precipitation and additive for the other three FWI drivers) using the approach described above (see Methods, section \\u003cem\\u003eDetrending and scaling factors \\u0026ndash; ERA5\\u003c/em\\u003e), taking preindustrial period 1850\\u0026ndash;1900 as the reference period. Then, the resulting monthly scaling factors were applied to ERA5 daily data to derive counterfactual meteorological drivers (as explained for ERA5 detrending in section above). Using these counterfactual meteorological drivers to recompute FWI allows for generating counterfactual FWI datasets in which long-term trends are removed from meteorological drivers. We then computed, for the scaling factors derived from each ensemble member of each model separately, the contribution of each driver to the annual-maximum extent of European land under extreme FWI as the difference between the annual maximum time series in factual against those of the counterfactual simulations. Such resulting annual differences derived from each model ensemble member were then averaged within each model, and finally combined into a multi-model mean. The above analysis was conducted in a case where all meteorological drivers were detrended simultaneously (for quantifying the total impact of climate change), and\\u0026mdash;to quantify individual driver contributions\\u0026mdash;in individual cases where only one meteorological driver at a time was detrended\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo evaluate the ability of climate models in reproducing long-term trends in comparison with ERA5, we additionally recomputed scaling factors using 1950\\u0026ndash;1980 as reference period, and computed the linear trends in the resulting factors of each meteorological driver separately for the period 1960\\u0026ndash;2015 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec and Supplementary Fig.\\u0026nbsp;5). Using this period allows us to evaluate trends from the exact same data years in CMIP6 and ERA5 (a more extended period would not suffice, as scaling factors are derived based on 21-years moving windows and ERA5 is available for the period 1950\\u0026ndash;2024 only).\\u003c/p\\u003e \\u003c/div\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eData availability\\u003c/h2\\u003e\\n \\u003cp\\u003eERA5 reanalysis data used in this study are publicly available from the Copernicus Climate Data Store (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cds.climate.copernicus.eu\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e).\\u003c/span\\u003e CMIP6 model outputs were obtained from the Earth System Grid Federation (ESGF) nodes (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://esgf-metagrid.cloud.dkrz.de/search\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e).\\u003c/span\\u003e The GFEDv4 burned area product is publicly accessible at \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.globalfiredata.org\\u003c/span\\u003e\\u003c/span\\u003e. E-OBS gridded observations are available from the European Climate Assessment and Dataset (ECA\\u0026amp;D) project (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ecad.eu/download/ensembles/download.php\\u003c/span\\u003e\\u003c/span\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e).\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eCode availability\\u003c/h2\\u003e\\n \\u003cp\\u003eAll computer codes used for the analyses are available from the authors upon request.\\u003c/p\\u003e\\n \\u003ch2\\u003e\\u003cstrong\\u003eAcknowledgments\\u0026nbsp;\\u003c/strong\\u003e\\u003c/h2\\u003e\\n \\u003cp\\u003eWe thank Yann Quilcaille for their initial input on the FWI calculation and for constructive feedback on the manuscript. The authors acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6, and the authors thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP and the ESGF. Analyses were carried out on the High-Performance Computing (HPC) Cluster EVE, a joint effort of both the Helmholtz Centre for Environmental Research \\u0026ndash; UFZ and the German Centre for Integrative Biodiversity Research (iDiv) Halle\\u0026ndash;Jena\\u0026ndash;Leipzig.\\u003c/p\\u003e\\n \\u003ch2\\u003eFunding Declaration\\u003c/h2\\u003e\\n \\u003cp\\u003eThis project received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via the Emmy Noether Programme (grant ID: 524780515). This project has received funding from the European Union\\u0026rsquo;s Horizon 2020 research and innovation programme under grant agreement No 101003469.\\u003c/p\\u003e\\n \\u003ch2\\u003e\\u003cstrong\\u003eAuthor contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/h2\\u003e\\n \\u003cp\\u003eEG and EB initiated the study and conceptualized the research; EB supervised the project; EG performed all analyses and created all figures; EG and EB wrote the paper, reviewed and edited the final manuscript.\\u003c/p\\u003e\\n \\u003ch2\\u003e\\u003cstrong\\u003eCompeting Interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/h2\\u003eThe authors declare no competing interests.\\n\\u003c/div\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWard, M. \\u003cem\\u003eet al.\\u003c/em\\u003e Impact of 2019\\u0026ndash;2020 mega-fires on Australian fauna habitat. \\u003cem\\u003eNat. Ecol. 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Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). \\u003cem\\u003eGeosci. Model Dev.\\u003c/em\\u003e 12, 3055\\u0026ndash;3070 (2019).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)\",\"snPcode\":\"44304\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44304/3\",\"title\":\"npj Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8583363/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8583363/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIntensifying fire-weather extremes increasingly threaten Europe, with recent wildfires linked to human-induced climate change. Yet, little is known about spatially compounding fire danger events\\u0026mdash;days when multiple regions simultaneously face extreme fire weather\\u0026mdash;which can trigger widespread fires and potentially overwhelm shared EU firefighting resources, amplifying impacts. Here, we analyse spatially compounding fire danger by combining burned area observations (2001\\u0026ndash;2015), ERA5-based Fire Weather Index (1950\\u0026ndash;2024), and CMIP6 climate simulations. We reveal that cross-country correlations in fire weather strongly enhance the likelihood of extremely widespread fire weather, with long-lasting compound hot-dry conditions acting as key meteorological drivers. The spatial extent of extreme fire weather has expanded markedly over the past three decades, primarily due to rising temperature and the associated decline in relative humidity. On average over the past decade, human-induced climate change contributed to the annual-maximum extent of European land synchronously experiencing extreme fire weather by 14.8% (4.8\\u0026ndash;25.6%, interquartile range across models). These results highlight the need for coordinated European adaptation to the growing potential for large-scale wildfires.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Human-induced climate change intensifies spatially compounding fire weather extremes across European countries\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-19 09:26:22\",\"doi\":\"10.21203/rs.3.rs-8583363/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-01-27T16:13:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-27T16:10:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-26T17:00:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-26T00:32:54+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"254063435282221013286513703396100272827\",\"date\":\"2026-01-17T17:38:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"301893226803094092809322006112120714570\",\"date\":\"2026-01-16T17:23:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"263511770958980949671398715940482188285\",\"date\":\"2026-01-14T19:25:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-01-14T16:13:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-01-14T15:11:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-01-14T14:44:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"npj Natural Hazards\",\"date\":\"2026-01-12T15:18:26+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)\",\"snPcode\":\"44304\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44304/3\",\"title\":\"npj Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"8e00b93f-6580-4217-a797-6061693d3544\",\"owner\":[],\"postedDate\":\"January 19th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":61320564,\"name\":\"Earth and environmental sciences/Climate sciences\"},{\"id\":61320565,\"name\":\"Earth and environmental sciences/Environmental sciences\"},{\"id\":61320566,\"name\":\"Earth and environmental sciences/Natural hazards\"}],\"tags\":[],\"updatedAt\":\"2026-04-22T14:26:32+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-8583363\",\"link\":\"https://doi.org/10.1038/s44304-026-00201-y\",\"journal\":{\"identity\":\"npj-natural-hazards\",\"isVorOnly\":false,\"title\":\"npj Natural Hazards\"},\"publishedOn\":\"2026-04-02 00:00:00\",\"publishedOnDateReadable\":\"April 2nd, 2026\"},\"versionCreatedAt\":\"2026-01-19 09:26:22\",\"video\":\"\",\"vorDoi\":\"10.1038/s44304-026-00201-y\",\"vorDoiUrl\":\"https://doi.org/10.1038/s44304-026-00201-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8583363\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8583363\",\"identity\":\"rs-8583363\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}