Abstract
This study quantifies how changes in temperature and precipitation would influence the intensity and duration of extreme fires across Europe. The analysis explores the impact of a range of climate change projections on fire events compared to a baseline of fire danger, using a 30-year ERA5 reanalysis. The results show that areas in southern Europe could experience a tenfold increase in the probability of catastrophic fires occurring in any given year under a moderate CMIP6 scenario. If global temperatures reach the +2 °C threshold, central and northern Europe will also become more susceptible to wildfires during droughts. The increased probability of fire extremes in a warming climate, in combination with an average one-week extension of the fire season across most countries, would put extra strain on Europe’s ability to cope in the forthcoming decades.
Similar content being viewed by others
Introduction
Wildfires are an essential ecological component in flammable ecosystems, and about 3% of the land area is burned every year1,2,3. While natural in origin, fire activity has been used for millennia by cultural and economic activities and to fulfill human desires to modify the landscape4,5. In Europe, 96% of all ignitions are caused by human activity, either intentionally or accidentally6. A large proportion of these fires are then suppressed to protect human lives and property7. The spread and intensity of wildfires are predominantly influenced by weather conditions, vegetation, and topography, regardless of the initial source of ignition.
In recent years, there has been a surge of destructive wildfires8, causing social disruptions and substantial economic losses9. Three of the worst fire years in terms of burnt area in Europe have occurred in the past six years since records began in 1980. In southern Europe, the total burnt area in 2021 was 391,736 ha, nearly double the previous year’s total and the highest since 2017. A shift in the scale and persistence of wildfires globally can be observed over the last 40 years10,11. Wildfires are occurring in new places because of increased vegetation flammability12. They happen outside the traditional fire season13,14 and are much more intense15,16. A small number of high-intensity wildfires, often called megafires17, produce most of the recorded burned areas(~97%)18,19. Their secondary impacts include erosion20, deforestation21,22, depletion of carbon stocks23,24, and they pose a threat to human safety and infrastructure25,26. These events are also of particular importance to wild-land fire management. Rapidly spreading fires are difficult to suppress and often have catastrophic impacts. No firefighting methods can fully cope with megafires, and these are often left to burn until natural extinction takes place27.
While several studies have examined climate-induced regional trends in fire danger17,28,29,30,31,32,33,34,35, a comprehensive quantitative analysis of how temperature and precipitation, both in isolation and combination, contribute to a change in the probability of extreme wildfire events at a pan-European scale remains limited. In this study, we address this research gap by performing a sensitivity analysis of how changes in precipitation and temperature influence fire danger. We use the fire weather index (FWI)36 calculated from the ECMWF fifth generation climate reanalysis (ERA5)37 to construct two-dimensional impact response surfaces (IRS) to quantify how temperature and precipitation variation will likely change fire extremes in Europe. FWI is an estimation of potential fire behavior and intensity based on prevailing weather conditions. Its calculation is based on daily weather variables, including air temperature and precipitation, and it is the most widely used metric for estimating fire weather globally, both operationally38 and in research15,39. While FWI does not directly express a measure of fire activity, it has been shown to correlate with burned areas, especially for significant events (burnt area > 10,000 hectares), and to indicate the effort required to suppress a fire once ignited40,41,42,43. Thus, one can anticipate an increased likelihood of extreme fires as extreme fire weather events become more frequent, regardless of the specific location where a fire might start. This correlation relies on the assumption of relative stability in fire ignitions. Most fires in Europe are ignited because of human behavior, and this behavior is not likely to change in the near future. We also assume that there will be no significant changes in fire suppression measures or land use, which could otherwise weaken the correlation between extreme FWI values and the occurrence of extreme fires. Control measures will face increasing challenges as fire risk escalates in extensive European areas44 and landscape transformations–such as rural abandonment–have led to increased fuel availability for burning45. Given these assumptions, the utilization of the output from the latest generation of Global Climate Models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), in conjunction with the FWI, has proven to be a valuable tool for comprehending the forthcoming alterations in fire weather patterns linked to a shifting climate46.
The area of focus in this study is a latitudinal gradient spanning three specific regions in mainland Europe, as delineated by the Intergovernmental Panel on Climate Change (IPCC, 2022) (Fig. 1). The three regions are projected to undergo variations in temperature and precipitation, characterized by warmer and drier summers in southern and central Europe (SEU and CEU) and warmer and wetter winters in northern Europe (NEU).
The CMIP6 simulations under a moderate Shared Socio-economic Pathway (SSP) scenario (SSP2-4.5) anticipate an average annual temperature rise in Europe of 2 °C in the short-term (2036−2065) and 3 °C in the long-term (2070−2099) (Supplementary Table 2). Under the more extreme SSP4-8.5 scenario, the increase could reach 4 °C. Such temperature rises, particularly when coupled with reduced precipitation, could significantly alter landscape flammability across Europe. The proposed IRS study quantifies the expected shifts in extreme fire probability in response to a range of projected temperature and precipitation changes. This analysis not only enhances our understanding of current and future fire risks but also aids in identifying European regions at high risk of catastrophic fires. Additionally, it offers a framework to assess the impact on the coping capacities of nations in a warmer climate.
Results
Fire extremes in Europe under current and future climate change
The initial step of the analysis involved assessing regional variations in landscape flammability and the prevalence of fire-prone conditions across Europe, considering different climatic zones. A cluster analysis of historical daily simulations (1981−2010) of FWI during the active fire season (April−October) calculated using the ERA5 reanalysis37 revealed five distinct regions (Fig. 1): boreal, temperate, semi-arid, alpine, and Mediterranean (MED). The identified regions represent areas of similar fire season length and intensity.
In the second step, the definition of an extreme fire event was established for each cluster. Specifically, an extreme event was characterized as corresponding to the FWI value expected to be observed once in 20 years, on average. The intensity of events at this level is assumed to be severe, potentially escalating into megafires. The 20-year return period differs significantly among the previously identified five regions. In the MED fire region, an FWI value of 63 represents the 20-year return period. This implies that between 1981 and 2010, there was a 5% probability of observing an FWI value equal to or greater than 63 in any given year (Fig. 1). However, a much smaller value of FWI denoted a 20-year return period in the boreal (FWI = 19) and alpine regions (FWI = 15).
The duration of the average fire season in different parts of Europe is also considered and defined as the number of days when FWI exceeds half the value of the 20-year return period threshold. Currently, the MED region is experiencing four months of fire weather conditions. This duration is as much as four times longer than the boreal, temperate and alpine regions and two times longer than the semi-arid regions of central Europe.
After identifying the intensity of extreme fire events as an FWI value corresponding to a 20-year return period and defining duration as the number of days above half of this threshold, we used these parameters as a reference under the current climate scenario. We then computed the exceedance probabilities in various future climate scenarios, assessing the likelihood of surpassing these established thresholds. This analysis aimed to quantify potential changes in the intensity and duration of extreme fire weather due to climate change. This assessment was conducted using IRS, where weather variables driving fire events are systematically altered, and the response in fire weather is depicted through surfaces representing combined changes (Figs. 2, 3).
The projected temperature and precipitation changes derived from CMIP6 multi-model simulations for the scenario SSP2-4.5 were used to calculate the likelihood of observing these changes in the short-term (Figs. 2, 3) and toward the end of the century (Supplementary Figs. 2, 3). The IRS was employed to directly link climate change outputs with potential impacts on the target variable (fire intensity and duration). The CMIP6 multi-model simulations consistently indicate comparable changes in precipitation across the five climate regions, while temperature changes exhibit greater variability among these regions. However, compared to temperature projections, the changes in precipitation are more uncertain47. This increased uncertainty arises from the significant spatial and temporal variability of precipitation, along with the complexity of atmospheric processes. Challenges in accurately modeling the hydrological cycle and cloud dynamics further contribute to this uncertainty. The NEU region exhibits significant variability in projected temperature increase, whereas precipitation shows relatively minor deviations compared to the current climate, with some climate models even suggesting an increase.
The impact of temperature changes on the probability of high FWI events varies across the five identified regions (Fig. 2). Arid regions, such as MED, exhibit a heightened sensitivity to temperature fluctuations, with a one-degree Celsius increase in temperature nearly doubling the likelihood of an extreme fire event. Precipitation decrease has a less pronounced impact. A 20% increase in total precipitation is required to counterbalance the effects of one-degree warming. A similar trend, albeit less pronounced, is evident in the semi-arid region of central Europe. In contrast, boreal, temperate and alpine regions display a distinct pattern whereby the increase in extreme fire events will be observed when temperature increases beyond the 2 °C threshold. Only beyond this point does a one-degree increase in temperature result in a twofold increase in the probability of extreme events.
In boreal and temperate regions (Fig. 2a, d), a 20% reduction in precipitation is expected to double the probability of extreme fire weather from its current 5% to 10%. In these regions, the impact of temperature changes on FWI is less pronounced. The increased probability of observing extreme events is accompanied by an extension of the fire season in Europe, with a nearly uniform 10-day increase observed when the mean temperature reaches +2 °C. However, the impact of this extension varies across European countries. Northern European countries are expected to witness a nearly 30% rise in the number of days requiring fire assistance, whereas southern European countries will experience a more moderate 8% increase (Fig. 3).
In the MED region, the majority of projections indicate a drying trend (Figs. 2, 3). However, the IRS analysis suggests that the potential of extreme fire risk in this region is primarily driven by temperature changes with limited sensitivity to changes in precipitation. This can be attributed to the region’s already dry conditions during summer, where further drying may have a limited impact on exacerbating the already high fire-prone conditions. Notably, any increase in temperature is expected to raise the probability of extreme fire occurrences in the future. While this analysis does not explicitly consider fuel moisture, it is important to note that in arid regions, elevated temperatures result in a higher partial vapor pressure deficit, which intensifies evaporation from soil and evapotranspiration from plants, ultimately reducing soil moisture and causing vegetation to become drier. Consequently, these conditions can increase the likelihood of the ignition and spread of fires due to the greater availability of dry fuels.
Considering CMIP6’s most likely scenario (denoted as a red dot in Figs. 2, 3) in both short- and long-term projections, the projected impact of climate change on landscape susceptibility to extreme fires varies across the five European regions (Fig. 4a, b). In most regions, about two-thirds (66%) of the landscape is predicted to have a 5−10% probability of such events in the short-term. However, the MED region is likely to face higher risks, with 40% of its area showing a 10−15% likelihood. Approximately 11% of the MED region could see extreme fire probabilities between 20% and 25%, while 5% might experience a 25−50% likelihood of extreme fires. By 2100, the extent of areas with a medium risk is expected to increase across all five regions. In the MED region, the area facing a medium risk is projected to triple, and in the other four regions, it is projected to double from 6% to 12%.
CMIP6’s most likely climate projections indicate an extended fire season duration across Europe (Fig. 4c, d). In the MED region, the fire season is expected to increase by at least one week, with one-fifth of this region potentially experiencing an extension of the fire season by 20 days by the end of the century (Fig. 4d). Similarly, the northern part of Europe is projected to experience a prolongation of the fire season by approximately one week. These projections consider changes in temperature and precipitation in isolation. However, a study by34 highlights the significance of maximum daily vapor pressure deficit in predicting fire activity. This study suggests that if the effect of moisture is considered, the fire season could extend beyond what our current analysis indicates.
An increase in the probability of extreme fires is expected to strain nations’ capacities to cope with the fire risk, particularly if it affects extensive areas. Climate risk information must be actionable to be useful for decision-makers. To expand on this, we assessed the spatial extent of the enhanced probability of extreme fires in short- and long-term scenarios (Fig. 5a), along with the extended periods of fire-prone weather (Fig. 5b).
Our analysis indicates that more than half of Europe’s total land area is expected to witness a doubling of the probability of extreme fire events, increasing from 5% to 10%. Moreover, specific regions will experience localized hotspots marked by a tenfold increase in the probability (from the current 5% to 50% under the most likely CMIP6 scenario).
The MED region, encompassing eastern Turkey, Greece, southern Italy, the Iberian Peninsula, southern France, and northern Africa, is projected to experience the most significant rise in fire risk (see Fig. 5). Additionally, a heightened risk zone extends along the Atlantic coastline of central and northern Europe. In contrast, northern Europe shows the least pronounced increase in risk. In most parts of this region, the expected maximum increase is 20% (shifting from one event every 20 years to one every five years). However, it is crucial to note that the boreal area is highly susceptible to changes in precipitation, and prolonged drought periods during the fire season can result in catastrophic consequences for this region.
In the short-term, the fire season is projected to be extended by 12−16 days in the Iberian Peninsula, eastern Spain, southern France, Greece, and western Turkey, while central Europe is expected to experience an increase of 4-8 days (Fig. 5). In the long-term, central Europe will see a modest increase of an additional four days, but the MED region, encompassing North Africa, eastern Spain, southern France, eastern Greece, and western Turkey, is anticipated to undergo a substantial escalation in fire duration, with increases ranging from 12 to 22 days. This trend indicates that, by the end of the century, up to 18% of southern Europe’s land area could be subjected to catastrophic fire events as frequently as every other year. Furthermore, climate projections reveal an emerging pattern of increased vulnerability to extreme fires in previously less affected regions of southern Europe (Fig. 5).
Discussion
Projected climate change, which encompasses changes in temperature and precipitation, is anticipated to have an impact on the occurrence of extreme fire events. In this study, we specifically concentrate on extreme fire weather conditions since they are known to be the primary drivers behind most large fire events48. Our investigation focuses on assessing the heightened likelihood of extreme fires occurring in Europe by analyzing the probability of observing FWI values equivalent to a 20-year return period in the current climate under future climate conditions. Although the FWI is not a direct measure of fire activity, it is closely linked to the environmental conditions conducive to fire development. This correlation is particularly pronounced for extreme events that span large areas, implying a strong connection between extreme fire weather and the probability of extreme fires.
Our analysis shows that there will be a substantial increase in the probability of these conditions developing under a broad spectrum of climate change conditions. Fires that develop at FWI larger than 50 (the current climate 20-year return period values of the MED and semi-arid regions) can transition into megafires. The consequences of these fires can be catastrophic, as demonstrated by recent events in Europe and worldwide. Notably, the Pedrógão Grande fire, which developed in Portugal49 in 2017, caused 66 casualties and triggered a change in the way fire suppression is managed by European member states across the European Union50. In recent years, a sharing mechanism known as RescEU has been implemented, providing a communal pool of resources, such as an expanded fleet of firefighting planes and ground forces, to enhance firefighting capabilities.
Fires that ignite at FWI around 20 are usually manageable through effective suppression actions. Even though the probability of a 20-year return period event is increasing in Europe’s temperate, boreal, and alpine regions, the likelihood of observing megafires in these regions, even under extreme projected climate changes, remains limited in absolute terms. However, a country’s coping capacity is often proportional to the frequency of extreme events. In 2018, when FWI reached its 20-year return period value, Scandinavia witnessed a substantial, albeit manageable, number of forest fires. These conditions were able to swamp the limited national resources. By the end of that year, a total area of 20,000 hectares had burned. This unprecedented event stunned the population and marked the worst fire year on record for northern Europe51.
The findings of this study highlight the MED region as experiencing the most pronounced impact in terms of increased fire extremes (Figs. 2–5). This is a direct consequence of the projected increase in temperature and decrease in precipitation over the fire season. Further, the IRS-based sensitivity analysis showed that the boreal and temperate cluster regions are very sensitive to changes in precipitation (Fig. 2a, d). In these regions, even areas that are typically less susceptible to fires may see an extension in the duration of fire-prone weather conditions. This effect can be exacerbated by the projected increase in fire ignitions, driven by climate-induced rises in lightning activity52. As a result, it is likely that even European boreal and temperate regions, which are expected to experience a moderate increase in fire extremes, will still confront a future marked by more frequent and intense wildfires.
Future increases in the drivers of wildfires caused by climate change are expected to be highly challenging to mitigate. They will impose additional burdens on the European community in managing future fire risks. RescEU is considered the EU’s response to the need to efficiently offset the consequences that the predicted increase in these events can have. RescuEU has proven to reduce the number of causalities by increasing the efficiency and the costs of suppression actions53. To a certain extent, investigating suppression capabilities can reduce the realized impacts44. This study, however, indicates that large parts of Europe will experience double the likelihood of these events happening, and some parts of southern Europe could experience a ten-fold increase in the probability of extreme fire weather (see also54,55).
The entire European continent will experience a lengthening of the fire season. Under these conditions, a tipping point can be reached due to the high costs of suppression strategies and the increased difficulties in dampening high-impact trajectories when these have an exponential growth7,56,57. In recent years, the recorded increase in megafires has already called for a paradigm shift to increase efforts toward mitigation rather than extinction measures58,59. Without mitigation, climate change is expected to increase the economic costs of fire suppression and may lead to fire seasons that overwhelm fire suppression agencies60. Although wildfire management is adapting to the new reality, more may be required in the future, given the extent of changes in extreme fire weather expected in Europe in future decades.
In this study, it is pertinent to acknowledge that our analysis does not account for vegetation dynamics, specifically the process of post-fire vegetation recovery. While we assume a minor change in the availability of fuel, it should be noted that if fuel availability were to diminish significantly, fire activity would be impeded, irrespective of the FWI conditions. Furthermore, this analysis presumes the absence of substantial climate change mitigation efforts, which, if enacted, could substantially alter the anticipated patterns and severity of fire activity in relation to changing FWI scenarios. Future research should consider incorporating vegetation dynamics under different climate scenarios to provide a more comprehensive understanding of the complex relationship between climate change and wildfires.
Methods
Data
The analysis was conducted using reanalysis data from ERA5 and global climate change projections from the CMIP6 multi-model dataset. ERA5 refers to the Fifth Generation of the European Reanalysis dataset produced by ECMWF and disseminated through the Copernicus Climate Data Store61. Reanalysis combines historical observations with advanced numerical weather prediction models to create a consistent and continuous representation of the Earth’s atmosphere over a specific period. ERA5 provides high-resolution, three-dimensional atmospheric data. It covers the entire globe with a spatial resolution of approximately 31 km and is available from 1940 to near-real-time with a latency of 2 months.
In our assessment of how changes in temperature and precipitation are likely to impact extreme fires in Europe, we overlaid the CMIP6 projections for comparison. The CMIP6 multi-model simulations, employing the SSP2-4.5 scenario62, provided a framework for examining potential future climates characterized by moderate greenhouse gas emissions mitigation and intermediate socio-economic development. Thirty-six commonly used climate models listed in Supplementary Table 1 were used. This study included multiple climate models to encompass various climate scenarios and enhance the reliability of our findings.
Fire weather index
To produce the historical daily FWI data outlined in this study, the global ECMWF Fire Forecast (GEFF) model41 forced by ERA5 data was used37. The dataset is available through the Copernicus Climate Data Store61. The study period spanned from 1981 to 2010, covering 30 years.
The FWI assesses potential fire danger by integrating key meteorological factors such as temperature, humidity, wind speed, and precipitation, providing a quantitative measure of landscape flammability. Our analysis has presumed a correlation between FWI and observed fire activity. However, the strength of this correlation varies significantly based on the specific environmental conditions and ecosystems under study. In areas where fire activity is predominantly limited by the availability of fuel, the link between FWI and fire occurrences tends to be weaker. This indicates that fire initiation relies more on the presence of flammable materials rather than solely on weather conditions38,41,63. Nevertheless, our analysis focuses on extreme fire weather scenarios where this correlation becomes notably stronger. During such conditions, the combination of high FWI values, dry weather, and strong winds creates an environment conducive to large and uncontrollable fires. Notably, certain regions only experience extreme fires when FWI values exceed a specific threshold. Recognizing these nuances and limitations is crucial when employing FWI as a proxy for fire activity, and our discussion section provides a comprehensive analysis of these results.
Return periods
We calculated the 20-year return periods of FWI to identify unusual fire conditions. Return periods are not commonly used in fire management. However, it is a common concept in other natural hazard assessments, such as flood and drought forecasting, to evaluate the rarity and intensity of a specific event. It is a statistical concept expressing the likelihood of an event occurring within a specified period. For example, a return period of 20 years denotes the value which would occur on average once in 20 years or with a probability of 5% in any given year. It is important to note that while the return period provides an average estimate, actual occurrences can be irregular and less predictable.
Return period, denoted as R, was computed using the Weibull’s formula as given below64:
where n is the total number of years of record and m is the rank of the maximum annual simulated FWI values when arranged in descending order. The probability of exceedance of FWI values is the reciprocal of the return period.
In our analysis, the twenty-year FWI return period serves as a critical threshold to assess the future risk of extreme fire weather events. We aim to evaluate how the probability of reaching or surpassing this threshold might shift under various climate scenarios. By establishing the current climatic conditions as a baseline, where the FWI threshold is met with a 5% probability each year, we project changes in this probability with altered temperature and precipitation changes.
Clustering fire regimes over Europe
The k-means clustering algorithm65 was used to cluster fire regimes across Europe. The analysis focused on the FWI time series data, from 1981 to 2010. The k-means clustering algorithm is a commonly used unsupervised machine learning technique that partitions data into K clusters based on their similarities.
Formally, clustering involves partitioning the set of observations \({{{\mathcal{L}}}}\) into K disjoint sets \({{{{\mathcal{L}}}}}_{1},\ldots ,{{{{\mathcal{L}}}}}_{K}\) by returning labels indicating the index of the class of membership of each observation. This algorithm requires prescribing the number of clusters K. The latter can be prescribed manually based on the user’s knowledge or estimated from a selection criteria such as the silhouette criterion66.
The k-means algorithm partitions the n observations into K clusters in which each observation belongs to the cluster with the nearest mean. It seeks to minimize the variance within the clusters:
where \({{{{{\mu }}}}}_{k}=\parallel {{{{\mathcal{L}}}}}_{k}{\parallel }^{-1}{\sum }_{i\in {{{{\mathcal{L}}}}}_{k}}{\tilde{{{{\bf{y}}}}}}^{(i)}\) is the empirical mean of \(\tilde{{{{\bf{y}}}}}\) in cluster \({{{{\mathcal{L}}}}}_{k}\).
The k-means clustering method was applied to the FWI time series data, covering the extended fire season from April to October in the northern hemisphere. An additional factor was added by calculating the product of the maximum FWI value and the FWI range (max*(max-min)) to account for fire intensity. The silhouette criterion was utilized to establish the optimal number of clusters for the k-means algorithm, evaluating the similarity of data points within clusters compared to those in neighboring clusters. This facilitated the identification of the most significant clusters in the dataset. Lastly, manual adjustments were made to refine the cluster definitions derived from the k-means analysis, ensuring an accurate representation of fire risk patterns and trends.
Derivation of incremental curves from CMIP6 multi-model projections
To evaluate the impact of climate change on fire events in Europe, the average inter-annual change signal, based on monthly means, in temperature and precipitation was derived from the CMIP6 multi-model simulations. The scenario SSP2-4.5 was used in this study because of its moderate emission trajectory, which we assume is a realistic response to the main atmospheric drivers (temperature and precipitation). To address the coarse resolution of the CMIP6 models, an upsampling approach was employed using ERA5 historical data. To compensate for the CMIP6 models’ coarse resolution, we applied an upsampling technique utilizing ERA5 historical data. This approach involved converting the CMIP6 models’ monthly outputs into daily values, achieved by integrating the more detailed resolution offered by the ERA5 dataset.
The forcing curves, shown in Supplementary Fig. 1, were derived by perturbing daily temperature (T) and precipitation (P) from ERA5 reanalysis over the control period 1980−2010 using monthly factors. These factors were calculated from the climate change signals in the CMIP6 multi-model simulations and integrated with mean annual perturbations. The perturbation for temperature T was constructed as an additive factor, varying from 0 °C to 6 °C with an increment of 1 °C, added to the projected temperature under the SSP2-4.5 climate scenario. The perturbation for precipitation was constructed as a multiplicative factor, ranging from 0.6 to 1.6 with an increment of 0.2. The analytical formulation of the incremental curves can be expressed as follows :
where T is temperature, P precipitation, X is the perturbation factor, i time (days), p projected values, \({\overline{T}}_{scen}\) future scenario projection of monthly mean temperature, \({\overline{T}}_{ctl}\) control period simulation of monthly mean temperature for j month of the year (Similar denotations for mean monthly precipitation). The methodology follows67. The resulting forcing data were then used to calculate FWI to create the impact response surfaces (see below section).
Impact response surfaces
IRS are an effective way to display an environmental system’s sensitivity to climate change68. The two-dimensional IRS facilitate the concurrent examination of two primary drivers affecting a single dependent variable. This approach enables a sensitivity analysis of the dependent variable, highlighting the influential forcing variables. In our case, the feature was the 20-year return period of FWI and the length of the fire season, expressed as days above half the 20-year return period. The exceedance probability is estimated through a frequency analysis of the feature over the training period. The final impact response curves are then plotted as point-wise exceedance probabilities for each perturbation, and continuous curves are interpolated between the points. This approach facilitates the identification of critical thresholds or tipping points beyond which the variable of interest may experience rapid or nonlinear changes. Additionally, the IRS methods aid in recognizing potential synergistic or antagonistic effects between the drivers, thereby informing adaptive management strategies and policy interventions.
Density-based probability of climate change projections
The shared socioeconomic pathway 2 (SSP2-4.5) scenario was considered to explore potential future developments in human society, the economy, and the environment, as well as their implications for greenhouse gas emissions and climate change. The SSP2-4.5 scenario is designed to represent a world with moderate challenges in terms of mitigation and adaptation to climate change.
Thirty-six distinct climate models (Supplementary Table 1) were considered to capture a wide range of potential future climate outcomes. This methodology emphasizes the importance of considering various factors and uncertainties, including greenhouse gas emissions, climate sensitivity, and natural climate variability. The results of these ensemble simulations form the basis for developing probability density functions (PDFs) of future climate variables, mainly temperature and precipitation.
Data availability
The baseline FWI data utilized in this study are accessible at the Copernicus Climate Data Store, available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview. The CMIP6 datasets can be downloaded from https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=doc. Furthermore, the ERA5 reanalysis data used in this study are available at the Copernicus Climate Data Store, accessible at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview. The perturbed FWI simulations data69, used for constructing the impact response surfaces, are available from a public Zenodo repository (https://doi.org/10.5281/zenodo.10458186).
Code availability
The Global ECMWF Fire Forecast (GEFF) fire model used to perform the simulations is accessible through the ECMWF Git repository at https://git.ecmwf.int/projects/CEMSF/repos/geff/browse. All additional codes needed to perform the analyses are available upon reasonable request from the corresponding author.
References
Randerson, J., Chen, Y., Van Der Werf, G., Rogers, B. & Morton, D. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Biogeosci. 117, G04012 (2012).
Giglio, L., Randerson, J. T. & Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (gfed4). J. Geophys. Res. Biogeosci. 118, 317–328 (2013).
Chuvieco, E. et al. A new global burned area product for climate assessment of fire impacts. Glob. Ecol. Biogeogr. 25, 619–629 (2016).
Bowman, D. M. et al. The human dimension of fire regimes on earth. J. Biogeogr. 38, 2223–2236 (2011).
Archibald, S. Managing the human component of fire regimes: lessons from africa. Philos. Trans. R. Soc. Lond. B Biol. Sci 371, 20150346 (2016).
Dijkstra, J., Durrant, T., San-Miguel-Ayanz, J. & Veraverbeke, S. Anthropogenic and lightning fire incidence and burned area in europe. Land 11, 651 (2022).
Fernandes, P. M., Pacheco, A. P., Almeida, R. & Claro, J. The role of fire-suppression force in limiting the spread of extremely large forest fires in portugal. Eur. J. For. Res. 135, 253–262 (2016).
Boer, M. M. et al. Changing weather extremes call for early warning of potential for catastrophic fire. Earth’s Future 5, 1196–1202 (2017).
Sadowska, B., Grzegorz, Z. & Stępnicka, N. Forest fires and losses caused by fires–an economic approach. WSEAS Trans. Environ. Dev 17, 181–191 (2021).
Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).
Dupuy, J.-l et al. Climate change impact on future wildfire danger and activity in southern europe: a review. Ann. For. Sci. 77, 1–24 (2020).
de Dios, V. R. et al. Climate change induced declines in fuel moisture may turn currently fire-free pyrenean mountain forests into fire-prone ecosystems. Sci. Total Environ. 797, 149104 (2021).
Moriondo, M. et al. Potential impact of climate change on fire risk in the mediterranean area. Clim. Res. 31, 85–95 (2006).
Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M. & Gowman, L. M. Implications of changing climate for global wildland fire. Int. J. Wildland Fire 18, 483–507 (2009).
Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).
Turco, M. et al. Exacerbated fires in mediterranean europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat. Commun. 9, 3821 (2018).
Pausas, J. G. & Keeley, J. E. Wildfires and global change. Front. Ecol. Environ. 19, 387–395 (2021).
Amiro, B. D. et al. Fire weather index system components for large fires in the canadian boreal forest. Int. J. Wildland Fire 13, 391–400 (2004).
Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western united states, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
Moody, J. A., Shakesby, R. A., Robichaud, P. R., Cannon, S. H. & Martin, D. A. Current research issues related to post-wildfire runoff and erosion processes. Earth-Sci. Rev. 122, 10–37 (2013).
Tepley, A. J., Thompson, J. R., Epstein, H. E. & Anderson-Teixeira, K. J. Vulnerability to forest loss through altered postfire recovery dynamics in a warming climate in the klamath mountains. Glob. Change Biol. 23, 4117–4132 (2017).
Walker, R. B., Coop, J. D., Parks, S. A. & Trader, L. Fire regimes approaching historic norms reduce wildfire-facilitated conversion from forest to non-forest. Ecosphere 9, e02182 (2018).
Hurteau, M. D. & Brooks, M. L. Short-and long-term effects of fire on carbon in us dry temperate forest systems. BioScience 61, 139–146 (2011).
North, M. P. & Hurteau, M. D. High-severity wildfire effects on carbon stocks and emissions in fuels treated and untreated forest. For. Ecol. Manag. 261, 1115–1120 (2011).
Collins, L. et al. The 2019/2020 mega-fires exposed australian ecosystems to an unprecedented extent of high-severity fire. Environ. Res. Lett. 16, 044029 (2021).
Parks, S. A. & Abatzoglou, J. T. Warmer and drier fire seasons contribute to increases in area burned at high severity in western us forests from 1985 to 2017. Geophys. Res. Lett. 47, e2020GL089858 (2020).
Wang, X. et al. Projected changes in daily fire spread across canada over the next century. Environ. Res. Lett. 12, 025005 (2017).
Grassi, G. et al. On the realistic contribution of european forests to reach climate objectives. Carbon Balance Manag. 14, 1–5 (2019).
Duane, A., Castellnou, M. & Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Climatic Change 165, 43 (2021).
Ellis, T. M., Bowman, D. M., Jain, P., Flannigan, M. D. & Williamson, G. J. Global increase in wildfire risk due to climate-driven declines in fuel moisture. Glob. Change Biol. 28, 1544–1559 (2022).
Jain, P., Castellanos-Acuna, D., Coogan, S. C., Abatzoglou, J. T. & Flannigan, M. D. Observed increases in extreme fire weather driven by atmospheric humidity and temperature. Nat. Clim. Chang. 12, 63–70 (2022).
Fronzek, S. et al. Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces. Clim. Risk Manag. 38, 100466 (2022).
Resco de Dios, V. et al. Convergence in critical fuel moisture and fire weather thresholds associated with fire activity in the pyroregions of mediterranean europe. Sci. Total Environ. 806, 151462 (2022).
Clarke, H. et al. Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand. Nat. Commun. 13, 7161 (2022).
Perry, M. C., Vanvyve, E., Betts, R. A. & Palin, E. J. Past and future trends in fire weather for the uk. Nat. Hazards Earth Syst. Sci. 22, 559–575 (2022).
Van Wagner, C. et al. Development and structure of the Canadian forest fire weather index system Vol. 35 (Canadian Forestry Service, Headquarters, Ottawa, 1987).
Vitolo, C. et al. ERA5-based global meteorological wildfire danger maps. Sci. Data. 7, 216 (2020).
Di Giuseppe, F. et al. Fire weather index: the skill provided by the european centre for medium-range weather forecasts ensemble prediction system. Nat. Hazards Earth Syst. Sci. 20, 2365–2378 (2020).
Touma, D., Stevenson, S., Lehner, F. & Coats, S. Human-driven greenhouse gas and aerosol emissions cause distinct regional impacts on extreme fire weather. Nat. Commun. 12, 212 (2021).
Wotton, B. & Martell, D. L. A lightning fire occurrence model for ontario. Can. J. For. Res. 35, 1389–1401 (2005).
Di Giuseppe, F. et al. The potential predictability of fire danger provided by numerical weather prediction. J. Appl. Meteorol. Climatol. 55, 2469–2491 (2016).
Jones, M. W. et al. Global and regional trends and drivers of fire under climate change. Reviews of Geophysics. 60, e2020RG000726 (2022).
Wotton, B. M. Interpreting and using outputs from the canadian forest fire danger rating system in research applications. Environ. Ecol. Stat. 16, 107–131 (2009).
Carnicer, J. et al. Global warming is shifting the relationships between fire weather and realized fire-induced co2 emissions in europe. Sci. Rep. 12, 1–6 (2022).
Fernandes, P. M., Loureiro, C., Guiomar, N., Pezzatti, G. B., Manso, F. T. & Lopes, Luís The dynamics and drivers of fuel and fire in the Portuguese public forest. J. Environ. Manage. 146, 373–382 (2014).
Gallo, C. et al. Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales. Geosci. Model Dev. 16, 3103–3122 (2023).
Jakob, C., Gettelman, A. & Pitman, A. The need to operationalize climate modelling. Nat. Clim. Chang. 13, 1158–1160 (2023).
Bowman, D. M. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058 (2017).
Viegas, D. X. et al. O complexo de incêndios de pedrógão grande e concelhos limítrofes, iniciado a 17 de junho de 2017. ADAI-CEIF, Coimbra (2017).
Bloem, S., Cullen, A. C., Mearns, L. O. & Abatzoglou, J. T. The role of international resource sharing arrangements in managing fire in the face of climate change. Fire 5, 88 (2022).
Krikken, F., Lehner, F., Haustein, K., Drobyshev, I. & van Oldenborgh, G. J. Attribution of the role of climate change in the forest fires in sweden 2018. Nat. Hazards Earth Syst. Sci. 21, 2169–2169 (2021).
Chen, Y. et al. Future lightning increase in the arctic: implications for fire and permafrost carbon. Nat. Clim. Chang. 11, 404–410 (2021).
Hopkins, W. J. & Faulkner, H. To The RescEU? Disaster Response As A Driver For European Integration. Tech. Rep., University of Canterbury. https://doi.org/10.26021/12400 (2021).
Solomon, S., Qin, D., Manning, M., Averyt, K. & Marquis, M. Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC Vol. 4 (Cambridge university press, 2007).
Brogli, R., Kröner, N., Sørland, S. L., Lüthi, D. & Schär, C. The role of hadley circulation and lapse-rate changes for the future european summer climate. J. Climate. 32, 385–404 (2019).
Amatulli, G., Camia, A. & San-Miguel-Ayanz, J. Estimating future burned areas under changing climate in the eu-mediterranean countries. Sci. Total Environ. 450, 209–222 (2013).
Ruffault, J. et al. Increased likelihood of heat-induced large wildfires in the mediterranean basin. Sci. Rep. 10, 13790 (2020).
Moreira, F. et al. Wildfire management in mediterranean-type regions: paradigm change needed. Environ. Res. Lett. 15, 011001 (2020).
Boer, M. M., Sadler, R. J., Wittkuhn, R. S., McCaw, L. & Grierson, P. F. Long-term impacts of prescribed burning on regional extent and incidence of wildfires–evidence from 50 years of active fire management in sw australian forests. For. Ecol. Manag. 259, 132–142 (2009).
Podur, J. & Wotton, M. Will climate change overwhelm fire management capacity? Ecol. Model. 221, 1301–1309 (2010).
Hersbach, H. et al. ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2023). (accessed 01 September 2022).
CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2021). (accessed 01 September 2022).
Di Giuseppe, F. Accounting for fuel in fire danger forecasts: the fire occurrence probability index (fopi). Environ. Res. Lett. 18, 064029 (2023).
Chow, V. T. Hand book of applied hydrology (McGraw-Hill Book Company, New York, 1964).
Likas, A., Vlassis, N. & Verbeek, J. The global k-means clustering algorithm. Pattern Recognit. 36, 451–461 (2003).
Rousseeuw, P. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
Wetterhall, F., Graham, L. P., Andréasson, J., Rosberg, J. & Yang, W. Using ensemble climate projections to assess probabilistic hydrological change in the nordic region. Nat. Hazards Earth Syst. Sci. 11, 2295–2306 (2011).
Jones, R. N. An environmental risk assessment/management framework for climate change impact assessments. Nat. Hazards. 23, 197–230 (2001).
30-Year Canadian Fire Weather Index Simulations over Europe: CMIP6-Informed Temperature and Precipitation Perturbations. Siham, El Garroussi Zenodo (2024). (accessed January 2024) https://doi.org/10.5281/zenodo.10458186.
Acknowledgements
This work has been developed under the framework of the EIFFEL project (funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101003518). The authors acknowledge the World Climate Research Program’s Working Group on Coupled Modeling for coordinating and promoting CMIP6. They thank the climate modeling groups for providing their model output and the Earth System Grid Federation (ESGF) for archiving and granting access to the data. They also appreciate the support from multiple funding agencies for CMIP6 and ESGF.
Author information
Authors and Affiliations
Contributions
F.W., S.E.G. and F.D.G. were responsible for conceptualization. S.E.G. and C.B. undertook data creation and model experimentation. S.E.G. and F.D.G. conducted the model analysis. F.W., S.E.G. and F.D.G. developed the methodology. F.D.G. and S.E.G. were responsible for visualization. F.D.G. and S.E.G. wrote the original draft and all other authors reviewed and edited the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
El Garroussi, S., Di Giuseppe, F., Barnard, C. et al. Europe faces up to tenfold increase in extreme fires in a warming climate. npj Clim Atmos Sci 7, 30 (2024). https://doi.org/10.1038/s41612-024-00575-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41612-024-00575-8
- Springer Nature Limited