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\begin{document}

\title{Global warming reshapes European pyroregions}



\author[1]{Luiz Felipe Galizia}%
\author[1]{Renaud Barbero}%
\author[2]{Marcos Rodrigues}%
\author[3]{Julien Ruffault}%
\author[3]{Francois Pimont}%
\author[1]{Thomas Curt}%
\affil[1]{INRAE RECOVER Aix-Marseille Univ}%
\affil[2]{University of Zaragoza}%
\affil[3]{INRAE}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Wildland fire is expected to increase in response to global warming, yet
little is known about future changes to fire regimes in Europe. Here, we
developed a pyrogeography based on statistical fire models to better
understand how global warming reshapes fire regimes across the
continent. We identified five large-scale pyroregions with different
levels of area burned, fire frequency, intensity, length of fire period,
size distribution, and seasonality. All other things being equal, global
warming was found to alter the distribution of these pyroregions, with a
spatial extension of the most fire prone pyroregions ranging
respectively from 50\% to 130\% under 2 and 4 \selectlanguage{ngerman}°C global warming
scenarios. Our estimates indicate a strong amplification of fire across
parts of southern Europe and subsequent shift towards new fire regimes,
implying substantial socio-ecological impacts in the absence of
mitigation or adaptation measures.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Global warming reshapes European pyroregions}

\textbf{L.F. Galizia\textsuperscript{1}, R. Barbero\textsuperscript{1},
M. Rodrigues\textsuperscript{2}, J. Ruffault\textsuperscript{3}, F.
Pimont\textsuperscript{3}, and T. Curt\textsuperscript{1}}

\textsuperscript{1}INRAE, RECOVER, Aix-Marseille Univ., Aix-en-Provence,
France.

\textsuperscript{2}Department of Geography and Land Management,
University of Zaragoza, GEOFOREST-IUCA Group, Spain.

\textsuperscript{3}INRAE, Ecologie des For\selectlanguage{ngerman}êts Méditerranéennes (URFM),
Avignon, France.

Corresponding author: L.F. Galizia (\emph{luiz.galizia@gmail.com)}

Key Points:

\begin{itemize}
\tightlist
\item
  This is the first study to project future changes in fire regimes on a
  pan-European scale under different global warming levels
\item
  Our projections point to an intensification and expansion of the most
  fire prone pyroregions in southern Europe under a warmer climate
\item
  Limiting global warming would substantially reduce the expansion of
  the area at risk and the transition towards more intense fire regimes
\end{itemize}

Abstract

Wildland fire is expected to increase in response to global warming, yet
little is known about future changes to fire regimes in Europe. Here, we
developed a pyrogeography based on statistical fire models to better
understand how global warming reshapes fire regimes across the
continent. We identified five large-scale pyroregions with different
levels of area burned, fire frequency, intensity, length of fire period,
size distribution, and seasonality. All other things being equal, global
warming was found to alter the distribution of these pyroregions, with a
spatial extension of the most fire prone pyroregions ranging
respectively from 50\% to 130\% under 2 and 4 °C global warming
scenarios. Our estimates indicate a strong amplification of fire across
parts of southern Europe and subsequent shift towards new fire regimes,
implying substantial socio-ecological impacts in the absence of
mitigation or adaptation measures.

\textbf{Plain Language Summary}

Previous research has investigated the effects of global warming
focussing on burned area only, ignoring other relevant fire metrics
which are strongly associated with the fire impacts. In this paper, we
examined the effects of global warming on a range of fire-regime
components including burned area, fire frequency, intensity,
seasonality, size, and length of the fire-prone window, which
collectively shape the so-called pyroregions. We identified five
large-scale pyroregions reflecting different fire regimes. Future
climate projections indicated an increase in all fire-regime components
and subsequent expansion of fire prone pyroregions across parts of
southern Europe under warmer and drier conditions. The most fire prone
pyroregions presented a spatial expansion ranging respectively from 50\%
to 130\% under 2 and 4 °C global warming scenarios with potential
impacts on society. Limiting global warming would substantially reduce
the expansion of the fire prone pyroregions in Europe.

1 Introduction

Wildland fire research has been increasingly promoted in Europe in
recent years to better understand the driving forces and identify
regions at risk. Fire activity responds to multiple drivers among
climate, vegetation, and human activities operating at different spatial
and temporal scales (Bowman et al., 2020; Cochrane \& Bowman, 2021;
Zheng et al., 2021). While the relative influence of environmental and
anthropogenic factors varies geographically, climate variability
expressed through fuel dryness has been shown to be the dominant driver
of fire activity at broad spatio-temporal scales (Abatzoglou et al.,
2018, 2021; Bedia et al., 2015). Warmer and drier conditions have been
shown to promote fire activity in many regions across Europe (Barbero et
al., 2019; Rodrigues et al., 2021; Turco et al., 2017). Extreme fire
seasons, featuring intense and large fires, as seen in 2016 in France
(Ruffault et al., 2018), 2017 in Portugal (Turco et al., 2019), and 2021
in Greece (Giannaros et al., 2022) were indeed associated with intense
droughts and heatwaves.

These fire climate conditions are widely thought to become more frequent
and intense with global warming (Abatzoglou et al., 2019; Jones et al.,
2022; Son et al., 2021). Previous research projected an increase in
burned area (Turco et al., 2018), fire frequency (Vilar et al., 2021),
fire intensity (Aparício et al., 2022), and fire size (Ruffault et al.,
2020) alongside a lengthening of the fire season (Fargeon et al., 2020)
in Europe, under a warmer climate. Yet, our understanding of the effects
of global warming on fire has been limited to single fire-regime
components, thereby ignoring how fire regimes might change in the
future.

Fire-regime components such as the frequency, intensity, seasonality,
and size control the effects of fire on the landscape, collectively
shaping the so-called pyroregions (Cochrane \& Bowman, 2021; Morgan et
al., 2001). Pyroregions are usually defined as broad spatio-temporal
units sharing similar distributions of the aforementioned components
(Krebs et al., 2010). In this sense, pyroregions provide a level of
generalization that may aid in understanding fire regimes among both
technical and non-technical audiences (Boulanger et al., 2013; Galizia
et al., 2021a). Pyroregions are also useful tools for developing fire
policies that aim to adapt burnable landscapes to future climate
conditions (Cochrane \& Bowman, 2021). While previous efforts have
focused on delineating historical or current pyroregions (Archibald et
al., 2013; Galizia et al., 2021a; Pausas, 2022; Rodrigues et al., 2020),
little is known about their future changes in response to global
warming. Here, we hypothesize that the future climate may not only
increase burned area but also alter the current pyrogeography with a
potential expansion of fire-prone regions and even the emergence of new
fire regimes.

Drawing from a remote-sensing dataset of individual fires, we developed
a European pyrogeography based on a range of fire-regime components to
better understand how, where and when global warming may reshape fire
regimes across the continent. We built empirical models linking each
fire-regime component with climate and environmental variables for the
historical period, and future 2°C and 4°C global warming scenarios. We
then delineated the pyroregions based on a clustering of the simulated
fire-regime components and examined how these pyroregions might change
in the future.

2 Materials and Methods

2.1 Fire data

We used the GlobFire (Artés et al., 2019) data, a daily remote sensing
dataset of individual fires built from the pixel-based burned area MODIS
product MCD64A1 Collection 6 (Giglio et al., 2018) at 500-m resolution
over the period 2001-2018. GlobFire provides information beyond the
burned area MODIS product, such as the perimeter and spatial extent of
each fire patch. GlobFire dataset presented a reasonable agreement with
ground-based fire data, especially for fires larger than 100 ha
(Campagnolo et al., 2021; Galizia et al., 2021b). We excluded fire data
located within artificial lands (i.e. agriculture and urban) using
Corine land cover data (European Union, 2018) because they generally do
not put ecosystems at risk. Additionally, we used daily fire radiative
power (FRP) of pixel-based MODIS product MCD14ML (Giglio, 2006) at 1-km
resolution over the period 2001-2018. The FRP measures the radiant
energy released per unit time from vegetation biomass burning (Wooster
et al., 2021) and has been extensively used as a proxy of fire intensity
(Archibald et al., 2013; Laurent et al., 2019; Pausas, 2022). Following
Laurent et al. 2019, we performed a spatio-temporal matching between FRP
and GlobFire databases at an annual timescale and 1-km resolution and
excluded FRP pixels without individual fire data.

2.2 Climate data

We used the observed fire-weather index (FWI) (Van Wagner, 1987) data
from the C3S Climate Data Store (CDS;
https://cds.climate.copernicus.eu/) at 25-km resolution over the period
1980-2018, given its strong correlations with fire activity across
Europe (Bedia et al., 2015; Galizia et al., 2021a; Pimont et al., 2021).
FWI is calculated using weather variables from the ECMWF ERA5 reanalysis
dataset (Vitolo et al., 2020). Simulated FWI were extracted from the CDS
at 11-km resolution over the period 1980-2098. Projections were computed
using one regional climate model coupled with six global climate models
(GCMs; Table S1) from the EURO-CORDEX (Jacob et al., 2014) initiative.
Given that much of the variability across models arises from GCMs, our
approach should capture most of the uncertainty in future projections.

We regridded the projected FWI onto a common 25-km resolution grid and
averaged both observed and projected FWIs onto an annual timescale. We
bias-corrected the projected FWI by applying the equidistant quantile
mapping (Li et al., 2010) method to each climate model. This ensures
that the distributions of projected FWI matched the observed FWI while
preserving future changes in FWI from this reference period. Using a
delta change bias correction procedure yielded similar results (Figure
S8). Note that we bias-corrected directly the FWI values to avoid an
underestimation of extreme values when correcting first the individual
meteorological variables (Jain et al., 2020). We then reaggregated
observed and projected fire weather data onto a common 50-km resolution
grid for fire modeling purposes.

We estimated the global warming dates (2 and 4 °C) for each climate
model following the procedure described in Jacob et al. (2014). Global
warming levels are largely independent of the choice of future emissions
scenario and aligned with the Paris agreement targets (Hausfather et
al., 2022). Warming levels correspond to the period over which
time-averaged global mean temperature (20-year window) reaches 2 and 4
°C, compared to the 'preindustrial' period 1881-1910 (Table S2).
Finally, we computed the multimodel mean by taking the average of the
FWI from the six climate models for each warming scenario.

2.3 Environmental data

We used the Corine land cover data from Copernicus Land Monitoring
Service (https://land.copernicus.eu/pan-european/corine-land-cover) at
100-m resolution from the period 2000-2018. We computed the land cover
distribution as the percentage area of the 50-km grid cell covered by
different vegetation and anthropogenic classes across Europe (Table S2).
To account for land cover changes through time we computed land cover
distributions averaging the Corine dataset over the studied period. We
omitted from our analysis grid cells with more than 80\% of non-burnable
land cover (i.e. anthropogenic lands), following Abatzoglou et al
(2019). Additionally, we retrieved topographic data from the GTOPO30
raster digital elevation model (https://earthexplorer.usgs.gov/) at 1 km
resolution. We computed the topographic slope as the percent of rise in
elevation calculated from the altitude layer and regridded onto a common
50-km resolution grid.

2.4 Fire-regime components

Fire-regime components represent the statistical fire characteristics
that collectively shape the so-called pyroregions (Krebs et al., 2010).
We aggregated daily fire data onto a 50-km grid at an annual timescale
to compute six fire-regime components: burned area (in ha), number of
fires (in n), percentage of large fires (fires \textgreater{} 100 ha; in
\%), percentage of fires during the cool season (fires in
November--April period; in \%), length of fire period (in months), and
fire intensity (in MW), following Galizia et al. (2021a) (see Table S3).
These components were used in previous studies for the characterization
of fire regimes (Archibald et al., 2013; Chuvieco et al., 2008; Pausas,
2022) and represent the spatial and temporal patterns of fire extent,
frequency, seasonality, intensity, and size distribution over the study
period.

2.5 Modeling fire-regime components

Statistical models linking climate and environmental conditions to fires
have received much attention under the global warming context
(Abatzoglou et al., 2021; Barbero et al., 2014; Pimont et al., 2021;
Riviere et al., 2022; Turco et al., 2018, 2019). We sought here to
develop individual statistical models for each fire-regime component to
simulate historical and future fire activity in Europe. We used
generalized additive models (GAMs), a supervised learning data modeling
method (James et al., 2013) that allows nonlinear responses to
explanatory variables to be estimated through different smoothed
functions and distribution types. GAMs were extensively used to simulate
fire-regime components, such as the area burned (Joseph et al., 2019;
Pimont et al., 2021) and fire frequency (Ager et al., 2018; Preisler et
al., 2008; Woolford et al., 2021). Each fire-regime component was
simulated at the annual scale in a 50-km grid with relevant explanatory
variables, such as climate, land cover, topography, and grid coordinates
(i.e. spatial effect) over the period 2001-2018 (Table S3). In order to
deal with the large proportions of zeros in our data, we used Tweedie
and negative binomial regression as GAMs to link the fire-regime
components with the explanatory variables (Wood et al., 2016). For more
technical details about smoothing and GAMs, see (Wood et al., 2016).
Note that we assumed that the percentage of fires during the cool season
will remain unchanged (i.e. stationary) in the future as no significant
relationship was found between this variable and climate conditions or
land cover types (Galizia et al., 2021a), indicating that these are
generally intentional fires under control. For each fire-regime
component model, we selected the most relevant explanatory variables
based on the stepwise approach based on a trade-off between accuracy and
complexity of the models using the Akaike information criterion (AIC).
Only variables with significant influence on a specific fire-regime
component were selected. We simulated each fire-regime component under
the historical period (2001-2018) and for two different global warming
levels (2 and 4 °C). For the future projections, we considered the
respective 20-year window of each model (Table S2). FWI was the only
time-varying explanatory variable in the models, the others were
considered stationary as FWI projections and land cover projections were
derived from different climate models.

We evaluated the predictive performance of the models with an
independent dataset i.e., excluding a test period of 5 years
(\textasciitilde{}30\% of the data) when computing the model parameters
(Turco et al., 2018). We compared model predictions with observations
aggregated across temporal and spatial scales to assess how the models
perform in practice. The goodness-of-fit between predictions and
observations was measured with the root-mean-square error (RMSE),
coefficient of determination (R\textsuperscript{2}), and its
significance values (\emph{p} ).

2.6 Delineating the European pyrogeography

We delineated the European pyrogeography based on the projections of
temporally averaged fire-regime components at the grid cell level over
both historical and future (20-years period) periods. The pyrogeography
was designed through a fuzzy version of the K-means clustering algorithm
(Pal et al., 1996). Fuzzy clustering algorithms have the advantage over
other clustering methods to provide the probability of each observation
to belong to a specific cluster. To do so, fire-regime components were
first rescaled into Z-scores with a zero mean and a unit variance, as
recommended in most clustering approaches (Galizia et al., 2021a;
Rodrigues et al., 2020). The clustering strategy consisted of a
Euclidean distance as a dissimilarity measure. The optimal number of
clusters was determined using the highest-ranked number of clusters out
of 30 indices available in the nbClust R package (Charrad et al., 2014).
We computed the spatial agreement between the pyrogeography from
observed and predicted fire-regime components.

2.7 Future changes in the European pyrogeography

We analyzed future changes in the spatial distribution of the
pyrogeography with 2 and 4°C global warming levels. To assess the
uncertainty of future climate projections, we simulated the
pyrogeography using each climate model separately, and grid cells for
which all models agreed on the simulated pyroregion were indicated with
a dot. Additionally, we examined the probability of pyroregions
occurrence to assess the degree to which each grid cell belongs to a
specific pyroregion for each scenario. We then computed the difference
in pyroregions probability between each warming scenario and the
historical period. Finally, we averaged probabilities across longitudes
and smoothed the signal using a polynomial filter to assess future
changes across a north-south gradient.

4 Results

4.1 Modeling fire-regime components

We built statistical models based on climate and environmental factors
for five different fire-regime components: burned area, number of fires,
percentage of large fires, length of fire period, and fire intensity.
These models reproduced to a large extent fire-regime components at the
grid-cell level (50-km at annual timescale) across the European
continent (Table S1). When averaged temporally over the historical
period (2001-2018), the spatial agreement between model outputs and
observations ranged from an R\textsuperscript{2} of 0.40 to 0.79
depending on fire-regime components (Figure 1A, Figure S1, and Table
S1), partly because of the presence of the spatial effect. When averaged
spatially across the continent, interannual correlations were however
much lower (R\textsuperscript{2} ranged from 0.22 to 0.43) (Table S1 and
Figure S2). This lower temporal agreement between observations and
simulations was expected due to the contrasted fire regimes within such
a large domain and the stochasticity at play amongst fire seasons. This
has however limited impact on our study given our objectives to
reproduce the averaged fire-regime components over 20-year periods.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Fire-regime components and partial effects of the statistical
fire models. (a) Observed and predicted burned area, number of fires,
percentage of large fires, length of fire period, and fire intensity
averaged over the historical period (2001--2018). R-squared
(R\textsuperscript{2}) represents the spatial agreement with
observations and significance level. Regions with more than 80\% of
non-burnable land cover are shaded in grey. Note the non-linear
colorscales. Observed percentage of fires during the cool season is
presented in Figure S3. (b) Response curves of the models showing the
effects of FWI, wildlands cover (\%), slope (\%), urban cover (\%), and
wildland-human interfaces (\%) on each fire-regime component. The
shading shows the 95\% confidence interval. Note that only
predictor/predicated couples with significant responses are shown. For
the spatial effect see Figure S4.

FWI was the dominant driver of all fire-regime components on such
spatio-temporal scales (Figure 1b). For instance, burned area, fire
intensity, length of fire period, and the number of fires were all
positively correlated with annual FWI, in agreement with previous
studies (Abatzoglou et al., 2018; Bedia et al., 2015; Ruffault et al.,
2020), but their responses seem to level off beyond a certain threshold,
as already observed at finer temporal and spatial scales (e.g. Pimont et
al., 2021). Overall, environmental factors, such as wildland cover and
topographic slope, also exerted a positive effect on fire activity as
documented in previous regional studies (Boulanger et al., 2018; Pimont
et al., 2021). Conversely, burned area and length of fire period were
found to decrease in regions where urban land cover exceeds 20\% due to
the fragmentation of the landscape decreasing fuel continuity and load
(Laurent et al., 2019). Interestingly, fire intensity also decreases at
wildland human interface exceeding 40\%, and at steeper slopes. Note
that the use of the spatial effect (grid coordinates) improved the
accuracy of the statistical fire models since this implicitly accounted
for interactions among the explanatory variables, which were not
explicitly modeled here.

4.2 Projecting future fire-regime components

We simulated each fire-regime component under both 2 and 4 \selectlanguage{ngerman}°C global
warming periods (20-year window) using the multimodel mean of FWI
computed from six paired GCM-RCMs projections while keeping the other
predictors stationary (Figure 2). As expected, FWI was projected to
increase in response to global warming, with the highest changes in the
Mediterranean basin and rather limited increases in northern Europe
(i.e. \textgreater{} 50º N) due to the future increase in summer
precipitation in response to large-scale circulation changes (de Vries
et al., 2022). The warm season may indeed become wetter across these
latitudes thereby dampening the effect of rising temperatures on the FWI
(Bedia et al., 2015; Carnicer et al., 2022; Krikken et al., 2021).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Observed FWI and future changes under different
global warming levels. (a) Mean annual FWI during the historical period
(2001-2018) and (b) absolute changes in the annual FWI multimodel mean
with respect to the historical period in response to a 2\selectlanguage{ngerman}°C and 4°C
global warming scenario.

All fire-regime components clearly increased across southern Europe in a
warmer world (Figure 3). Regions such as the northwest of the Iberian
Peninsula and the western Balkans presented substantial changes under
the 2 °C global warming scenario. Larger increases in fire activity were
foreseen under the 4 °C warming scenario, with a lengthening of the
historical fire season by about 3 months in northern Portugal and
western Balkans. Other regions, such as northern Spain, western
Pyrenees, and southern Italy, showed substantial changes as well in that
scenario. Similar to (Turco et al., 2018), we found an increase in the
burned area exceeding 50\% across the northern Iberian Peninsula beyond
a 2°C global warming level (Figure S5). Alongside the burned area, our
analysis showed large increases in fire frequency, fire intensity, the
length of fire season, and percentage of large fires. Yet, there was no
notable increase in fire activity across central and northern Europe
(i.e. \textgreater{} 50º N) due to the limited change in FWI.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Changes in projected fire-regime components under
different global warming levels. Absolute changes in projected
fire-regime components in response to a 2\selectlanguage{ngerman}°C and 4°C global warming
scenario with respect to the historical period (2001-2018).

Although changes in fire-regime components are mostly expected across
southern Europe due to the large signal of change in the FWI, the
spatial patterns of changes did not entirely match those of the FWI (see
Figure 2 and 3) as the climate-fire relation is mediated, on finer
scales, by other bottom-up drivers.

4.3 Historical and future European pyrogeography

We then delineated the European pyrogeography based on a clustering of
the temporally averaged fire-regime components over both the historical
and future periods. We identified five different pyroregions
representative of fire regimes prevailing in Europe (Figure 4). A
Cool-season fire pyroregion (hereafter CSF) is characterized by moderate
fire activity and with a large percentage of very low-intensity fires
occurring during the November--April period (Figure 4d). A Low
fire-prone pyroregion (hereafter Low-FP) is characterized by very low
fire activity and dominated by low-intensity fires. A Fire-prone
pyroregion (hereafter FP) is characterized by moderate fire activity
with moderate fire intensity, and a high proportion of large fires. A
Highly fire-prone pyroregion (hereafter High-FP) features a high fire
occurrence with high fire intensity and a long fire period. Finally, an
Extremely fire-prone pyroregion (hereafter Extremely-FP) displays the
highest fire incidence, fire intensity, and the longest fire period,
characterizing the most fire-affected region in Europe. Note that FP,
High-PF, and Extremely-FP presented a substantial percentage of
cool-season fires (\textasciitilde{}10\%), suggesting a bimodal fire
season as seen in other regional analyses (Benali et al., 2017; Pimont
et al., 2021). Conversely, in Low-FP, all fires occurred during the warm
period.

Over the historical period, the CSF was scattered across Europe,
including parts of the Alps, Pyrenees, Scotland, Romania, and the
Baltics (Figure 4a). The Low-FP was found mostly across northern and
parts of central Europe. The FP was identified mostly across Spain,
southern Portugal, southern France, Italy, and parts of the Balkans. The
High-FP was found in the northwestern part of the Iberian Peninsula,
Sicily, and parts of the Balkans. Finally, the Extremely-FP was located
mostly in northern Portugal. This historical pyrogeography built from
modeled fire-regime components presented a reasonable spatial agreement
(i.e. 86\% of all grid cells were correctly classified) when compared
with the pyrogeography built from observed fire-regime components (see
Figure S6). Additionally, this pyrogeography exhibited spatial patterns
in line with those reported in previous regional studies in southern
Europe (Calheiros et al., 2021; Fréjaville \& Curt, 2017; Moreno \&
Chuvieco, 2013; Rodrigues et al., 2020).

In the 2°C global warming scenario, the spatial extent of High-FP and
Extremely-FP expanded by 71\% and 43\%, while Low-FP and FP decreased by
\textasciitilde{} 2\% and 6\%, respectively (Figure 4b). More acute
changes arose with a 4°C warming, with High-FP and Extremely-FP
increasing up to 197\% and 129\% in extent, while Low-FP, FP, and CSF
decreased by \textasciitilde{} 5\%, 7\%, and 21\%, respectively (Figure
4c). In absolute terms, High-FP and Extremely-FP together increased by
116,410 km2 in a 2°C warming and 324,285 km2 in a 4°C warming. This
represents an expansion of 1 to 3 times the size of Portugal. Overall,
the main transitions occurred across southern Europe, with less
fire-prone pyroregions (Low-FP and CSF) switching to more fire-prone
pyroregions (FP and High-FP) and fire-prone (FP) switching to higher
fire-prone pyroregions (High-FP and Extremely-FP), indicating an
intensification of fire activity in regions already at risk (see Figure
S7).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Historical and future pyrogeography under different
global warming levels. Projected pyrogeography based on simulated
fire-regime components for (a) the historical period (2001-2018), (b)
the 2\selectlanguage{ngerman}°C, and (c) 4°C global warming scenarios. Values in the top left
represent the relative extent of each pyroregion and relative changes
(in \%) in pyroregion extents among the scenarios. Dots indicate grid
cells where the pyrogeography agrees with all individual climate model
projections. (d) Distribution of fire-regime components (i.e. median and
interquartile range) in each pyroregion.

For a deeper understanding of future potential switches induced by
climate change, we also examined, for each warming scenario, how the
probabilities of grid cells to be classified in a given pyroregion may
change (Figure 5). Unlike categorical changes (i.e. hard clustering)
seen in Figure 4, which were mostly clumped in specific regions of
southern Europe, large changes in the probability of pyroregions
occurrence emerged along the northern edge of historically fire-prone
regions (i.e. 40-45º N). We found an increased probability of FP
expanding towards the north, while High-FP may expand to the east and
south. However, future increases in FWI were too limited to trigger
categorical changes in more mesic forested zones such as central and
northern Europe.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Changes in probability to belong to each pyroregion
under different global warming levels. (a) Absolute changes in
pyroregions probability were computed for each warming scenario with
respect to the historical period (2001-2018). The probability of
occurrence (0-1) indicates the degree to which grid cells belong to each
pyroregion and (b) Changes in the latitudinal average probability
computed from weighted regression (smooth) across the latitudinal
gradients for each warming scenario.

Building upon previous studies projecting an increase in fire frequency
and burned area across southern Europe due to global warming (Dupuy et
al., 2020; Ruffault et al., 2020; Turco et al., 2018), our study
provided two important new insights. First, we considered a range of
fire-regime components, going beyond the single burned area metric
examined in most studies. By including fire frequency, intensity, size
distribution, and seasonality we presented different spatial patterns of
fire that have been shown to shape collectively the pyroregions (Bowman
et al., 2020; Krebs et al., 2010). For instance, we found that fire
regimes in the southern Iberian peninsula were dominated by large but
less frequent fires than in northern Portugal which featured the highest
fire activity in Europe. In mountainous and/or traditionally
agricultural regions, such as the Pyrenees, parts of the Alps, and
Scotland, burned area can be substantial but originates mostly from
cool-season fires due to human-related activities, which were not found
to be related to climate conditions (Galizia et al., 2021a).
Additionally, the magnitude of future changes was found to vary
substantially across the fire-regime components (Figure S5). The highest
changes were found in fire intensity and percentage of large fires,
while changes in the number of fires were more limited. Second, we
projected future changes in pyroregions in a spatially and temporally
explicit approach at a pan-European level, relying on a statistical
modeling framework able to reproduce historical patterns. Spatially and
temporally explicit studies provide an optimal view of fire regimes
being more relevant for fire management since they indicate where and
when changes may occur (Boulanger et al., 2013; Rodrigues et al., 2020).

Our findings highlighted the importance of climate as a primary control
of fire regimes, as observed in previous studies examining burned area
(Abatzoglou et al., 2018; Jones et al., 2022; Rogers et al., 2020), but
also indicated that climate alone cannot explain all of the variation in
fire regimes throughout Europe. Other factors, such as the location,
land cover, urban cover and topography controlled to some extent fire
regimes across space. Future changes projected in the European
pyrogeography agreed with other studies indicating that most of the
future increases are expected in the most fire-affected areas today
(Carnicer et al., 2022; Jones et al., 2022; Riviere et al., 2022).
Additionally, our findings indicated that regions with a great extent of
fuel available to burn in the transition zones (40-45\selectlanguage{ngerman}° N) were more
likely to shift towards a more fire prone regime in a warmer and drier
climate.

This work extends previous regional or national studies that had
delineated historical fire regimes across parts of Europe (Fréjaville \&
Curt, 2017; Resco de Dios et al., 2022; Rodrigues et al., 2021) and
shows how global warming might alter fire regimes in Europe, providing
valuable insights into the implementation of relevant policies on a
continental scale. We reported on a strong intensification and expansion
of the most fire prone regions (High-FP and Extremely-FP) across
southern Europe in a warmer world. This shed light on potential concerns
raised by firefighting and fire management services, which were devised
based on historical records or experiences. An increase in the area
burned, fire intensity, and lengthening of fire period up to 3 months in
parts of the Balkans, northern Iberian Peninsula, Italy, and western
France may overwhelm national fire suppression capacities. Observations
alone may become insufficient to cope with fire in a warmer climate in
some regions of Europe (Taylor, 2020). In this sense, the pyrogeography
developed here may help in prioritizing fire management and develop
consistent risk mitigation strategies across pyroregions. Pyroregions
combined with fire danger forecasts can be seen as broad management
units to mitigate the negative effects of fire in the short term.
Additionally, it may also facilitate country-to-country cooperation for
fire management and suppression (Bloem et al., 2022) when pyroregions
span geopolitical borders, fostering and strengthening partnerships
among fire-affected regions within the European Union Civil Protection
Mechanism. Finally, combining the pyrogeography with exposure and
vulnerability maps would be the first step into a fire risk assessment
on a pan-European scale.

The classification of fire-regime components into pyroregions is widely
thought to capture the spatial heterogeneity of fire regimes providing a
level of generalization that aids in understanding the fire patterns
(Boulanger et al., 2013; Bowman et al., 2020). This implies using a
coarse spatiotemporal resolution in order to identify persistent fire
patterns (i.e. historical range of variability). However, fires are
often characterized by many low-intensity events and a few
high-intensity events responsible for most of the societal and
ecological impacts (Le Breton et al., 2022). The latter is obviously
masked in such coarse resolution analysis (Krebs et al., 2010). Our
approach is thus likely to underestimate the occurrence of individual
extreme fire events generally associated with specific meteorological
conditions (Ruffault et al., 2020). Flash droughts and/or critical
synoptic-scale fire weather conditions facilitate the occurrence of
extreme fire on sub-annual timescales, features that are not evident in
annual resolution (Barbero et al., 2019; Pimont et al., 2021).
Additionally, climate projections are known to underestimate the
observed trends in fire weather conditions across Europe (Jones et al.,
2022). In this sense, our study should be viewed as a conservative
estimate of the effect of climate change on fire regimes. We note that
the methodology developed here has some other limitations. First, we
assumed that the percentage of cool-season fires will remain unchanged
in the future. In Europe, cool-season fires are mostly related to
anthropogenic activities, however, no correlation was found between
those fires and anthropogenic variables over the historical period,
hampering reliable projections. Second, we considered the environmental
and human-related variables as stationary in our future simulations.
Indeed, a warming climate may temper increases in fire activity by
decreasing fuel availability in dry regions through aridification (Mauri
et al., 2022; Pausas \& Paula, 2012). Conversely, this may boost fire
activity in other regions through transitions from forested systems to
more flammable vegetation types (i.e. shrublands), or through increasing
dead fuel from drought-induced forest diebacks (Liang et al., 2017;
Masrur et al., 2022). Additionally, an increase in fuel accumulation due
to systematic fire suppression (Moreira et al., 2020; Parisien et al.,
2020) could exacerbate the signal of climate change on fire activity,
particularly high-intensity fires. To overcome these limitations,
studies that explicitly account for interactions among fire, climate,
vegetation, and anthropogenic factors have been implemented using
dynamic global vegetation models (Hantson et al., 2016). Yet, such
models often struggle to represent interannual variations in fire
activity and observed trends (Forkel et al., 2019; Jones et al., 2022).
Finally, previous research has shown that new fire suppression policies
may be able to reshape the functional climate-fire relationship (e.g.
Ruffault \& Mouillot, 2015). In this sense, continued efforts are still
needed to better understand the roles played by top-down climate and
bottom-up environmental and anthropogenic factors in shaping current and
future fire regimes across Europe.

5 Conclusions

This work is the first to project future changes in fire regimes on a
pan-European scale. The developed pyrogeography synthesized the
complexity of fire patterns enabling a better understanding of the
pan-European fire regimes. This is crucial in the context of global
change since it provides a baseline to investigate temporal and spatial
changes in fire regimes under different warming scenarios. Additionally,
by examining future changes under policy-relevant warming levels of 2°C
and 4°C, we provided insights into how the success or failure of climate
policies would translate to fire hazards in Europe.

In summary, we found a substantial increase in all fire-regime
components across southern Europe in a future warmer climate, indicating
a strong amplification of fire in regions already at risk. We showed
that under global warming, pyroregions are likely to shift towards more
fire prone regimes across parts of southern Europe, potentially
triggering a wide range of ecological and socio-economic issues.
Additionally, regions on the northern edge of historically fire-prone
areas (i.e. 40-45º N) were found to be the most sensitive to a warming
climate.

These projected changes have direct implications for both short-term
risk management, long-term risk mitigation implemented by the European
Union Civil Protection mechanisms, as well as climate adaptation across
these regions. This notably includes increased community preparedness,
optimized resource allocation (personnel and equipment), resource
sharing, and enhanced fuel management. Policies based on a specified
fire-regime target should help develop better fire prevention and
suppression strategies supporting fire managers to minimize the negative
impacts of fire.

\textbf{Acknowledgments}

This work was funded by the project MED-Star, supported by the European
Union under the Operational Program Italy/France Maritime (project No
CUP E88H19000120007). This work was partially funded by project
FIREPATHS (PID2020-116556RA-I00), supported by the Spanish Ministry of
Science and Innovation.

\textbf{Data Availability Statement}

All the data that support this study can be freely accessed using the
websites or data repositories described below. The GlobFire dataset of
individual fires is available at
https://doi.pangaea.de/10.1594/PANGAEA.895835. The fire radiative power
from MODIS (MCD14DL) is available at https://earthdata.nasa.gov/firms.
The Canadian FWI System indices from ERA5 reanalysis are available at
https://doi.org/10.24381/cds.0e89c522 and from EURO-CORDEX climate
projections are available at https://doi.org/10.24381/CDS.CA755DE7. The
land cover dataset is available at
https://land.copernicus.eu/pan-european/corine-land-cover. The GTOPO30
global elevation data is available at https://doi.org/10.5065/A1Z4-EE71.

\textbf{References}

Abatzoglou, J. T., Battisti, D. S., Williams, A. P., Hansen, W. D.,
Harvey, B. J., \& Kolden, C. A. (2021). Projected increases in western
US forest fire despite growing fuel constraints. Communications Earth \&
Environment, 2(1), 227. https://doi.org/10.1038/s43247-021-00299-0

Abatzoglou, J. T., Williams, A. P., \& Barbero, R. (2019). Global
Emergence of Anthropogenic Climate Change in Fire Weather Indices.
Geophysical Research Letters, 46(1), 326--336.
https://doi.org/10.1029/2018GL080959

Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M., \&
Kolden, C. A. (2018). Global patterns of interannual climate-fire
relationships. Global Change Biology, 24(11), 5164--5175.
https://doi.org/10.1111/gcb.14405

Ager, A. A., Barros, A. M. G., Day, M. A., Preisler, H. K., Spies, T.
A., \& Bolte, J. (2018). Analyzing fine-scale spatiotemporal drivers of
wildfire in a forest landscape model. Ecological Modelling, 384,
87--102. https://doi.org/10.1016/j.ecolmodel.2018.06.018

Aparício, B. A., Santos, J. A., Freitas, T. R., Sá, A. C. L., Pereira,
J. M. C., \& Fernandes, P. M. (2022). Unravelling the effect of climate
change on fire danger and fire behaviour in the Transboundary Biosphere
Reserve of Meseta Ibérica (Portugal-Spain). Climatic Change, 173(1--2),
5. https://doi.org/10.1007/s10584-022-03399-8

Archibald, S., Lehmann, C. E. R., Gomez-Dans, J. L., \& Bradstock, R. A.
(2013). Defining pyromes and global syndromes of fire regimes.
Proceedings of the National Academy of Sciences, 110(16), 6442--6447.
https://doi.org/10.1073/pnas.1211466110

Artés, T., Oom, D., de Rigo, D., Durrant, T. H., Maianti, P., Libertà,
G., \& San-Miguel-Ayanz, J. (2019). A global wildfire dataset for the
analysis of fire regimes and fire behaviour. Scientific Data, 6(1), 296.
https://doi.org/10.1038/s41597-019-0312-2

Barbero, R., Abatzoglou, J. T., Steel, E. A., \& K Larkin, N. (2014).
Modeling very large-fire occurrences over the continental United States
from weather and climate forcing. Environmental Research Letters, 9(12),
124009. https://doi.org/10.1088/1748-9326/9/12/124009

Barbero, R., Curt, T., Ganteaume, A., Maillé, E., Jappiot, M., \&
Bellet, A. (2019). Simulating the effects of weather and climate on
large wildfires in France. Natural Hazards and Earth System Sciences,
19(2), 441--454. https://doi.org/10.5194/nhess-19-441-2019

Bedia, J., Herrera, S., Gutiérrez, J. M., Benali, A., Brands, S., Mota,
B., \& Moreno, J. M. (2015). Global patterns in the sensitivity of
burned area to fire-weather: Implications for climate change.
Agricultural and Forest Meteorology, 214--215, 369--379.
https://doi.org/10.1016/j.agrformet.2015.09.002

Benali, A., Mota, B., Carvalhais, N., Oom, D., Miller, L. M.,
Campagnolo, M. L., \& Pereira, J. M. C. (2017). Bimodal fire regimes
unveil a global-scale anthropogenic fingerprint: Benali et al. Global
Ecology and Biogeography, 26(7), 799--811.
https://doi.org/10.1111/geb.12586

Bloem, S., Cullen, A. C., Mearns, L. O., \& Abatzoglou, J. T. (2022).
The Role of International Resource Sharing Arrangements in Managing Fire
in the Face of Climate Change. Fire, 5(4), 88.
https://doi.org/10.3390/fire5040088

Boulanger, Y., Gauthier, S., Gray, D. R., Le Goff, H., Lefort, P., \&
Morissette, J. (2013). Fire regime zonation under current and future
climate over eastern Canada. Ecological Applications, 23(4), 904--923.
https://doi.org/10.1890/12-0698.1

Boulanger, Y., Parisien, M.-A., \& Wang, X. (2018). Model-specification
uncertainty in future area burned by wildfires in Canada. International
Journal of Wildland Fire, 27(3), 164. https://doi.org/10.1071/WF17123

Bowman, D., Kolden, C. A., Abatzoglou, J. T., Johnston, F. H., van der
Werf, G. R., \& Flannigan, M. (2020). Vegetation fires in the
Anthropocene. Nature Reviews Earth \& Environment, 1(10), 500--515.
https://doi.org/10.1038/ s43017-020-0085-3

Calheiros, T., Pereira, M. G., \& Nunes, J. P. (2021). Assessing impacts
of future climate change on extreme fire weather and pyro-regions in
Iberian Peninsula. Science of The Total Environment, 754, 142233.
https://doi.org/10.1016/j.scitotenv.2020.142233

Campagnolo, M. L., Libonati, R., Rodrigues, J. A., \& Pereira, J. M. C.
(2021). A comprehensive characterization of MODIS daily burned area
mapping accuracy across fire sizes in tropical savannas. Remote Sensing
of Environment, 252, 112115. https://doi.org/10.1016/j.rse.2020.112115

Carnicer, J., Alegria, A., Giannakopoulos, C., Di Giuseppe, F., Karali,
A., Koutsias, N., Lionello, P., Parrington, M., \& Vitolo, C. (2022).
Global warming is shifting the relationships between fire weather and
realized fire-induced CO2 emissions in Europe. Scientific Reports,
12(1), 10365. https://doi.org/10.1038/s41598-022-14480-8

Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A., \& Charrad, M. M.
(2014). Package `nbclust.' Journal of Statistical Software, 61(6),
1--36.

Chuvieco, E., Giglio, L., \& Justice, C. (2008). Global characterization
of fire activity: Toward defining fire regimes from Earth observation
data. Global Change Biology, 14(7), 1488--1502.

Cochrane, M. A., \& Bowman, D. M. J. S. (2021). Manage fire regimes, not
fires. Nature Geoscience, 14(7), 455--457.
https://doi.org/10.1038/s41561-021-00791-4

de Vries, H., Lenderink, G., van der Wiel, K., \& van Meijgaard, E.
(2022). Quantifying the role of the large-scale circulation on European
summer precipitation change. Climate Dynamics.
https://doi.org/10.1007/s00382-022-06250-z

Dupuy, J., Fargeon, H., Martin-StPaul, N., Pimont, F., Ruffault, J.,
Guijarro, M., Hernando, C., Madrigal, J., \& Fernandes, P. (2020).
Climate change impact on future wildfire danger and activity in southern
Europe: A review. Annals of Forest Science, 77(2), 35.
https://doi.org/10.1007/s13595-020-00933-5

European Union. (2018). Copernicus Land Monitoring Service (2018).
European Environment Agency (EEA). https://land.copernicus.eu/

Fargeon, H., Pimont, F., Martin-StPaul, N., De Caceres, M., Ruffault,
J., Barbero, R., \& Dupuy, J.-L. (2020). Projections of fire danger
under climate change over France: Where do the greatest uncertainties
lie? Climatic Change, 160(3), 479--493.
https://doi.org/10.1007/s10584-019-02629-w

Forkel, M., Andela, N., Harrison, S. P., Lasslop, G., van Marle, M.,
Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F.,
Melton, J., Sitch, S., Yue, C., \& Arneth, A. (2019). Emergent
relationships with respect to burned area in global satellite
observations and fire-enabled vegetation models. Biogeosciences, 16(1),
57--76. https://doi.org/10.5194/bg-16-57-2019

Fréjaville, T., \& Curt, T. (2017). Seasonal changes in the human
alteration of fire regimes beyond the climate forcing. Environmental
Research Letters, 12(3), 035006.
https://doi.org/10.1088/1748-9326/aa5d23

Galizia, L. F., Curt, T., Barbero, R., \& Rodrigues, M. (2021a).
Understanding fire regimes in Europe. International Journal of Wildland
Fire. https://doi.org/10.1071/WF21081

Galizia, L. F., Curt, T., Barbero, R., \& Rodrigues, M. (2021b).
Assessing the accuracy of remotely sensed fire datasets across the
southwestern Mediterranean Basin. Natural Hazards and Earth System
Sciences, 21(1), 73--86. https://doi.org/10.5194/nhess-21-73-2021

Giannaros, T. M., Papavasileiou, G., Lagouvardos, K., Kotroni, V.,
Dafis, S., Karagiannidis, A., \& Dragozi, E. (2022). Meteorological
Analysis of the 2021 Extreme Wildfires in Greece: Lessons Learned and
Implications for Early Warning of the Potential for Pyroconvection. 13.

Giglio, L. (2006). Global estimation of burned area using MODIS active
\selectlanguage{english}fire observations. Atmos. Chem. Phys., 18.

Giglio, L., Boschetti, L., Roy, D., Hoffmann, A. A., Humber, M., \&
Hall, J. V. (2018). Collection 6 MODIS Burned Area Product User's Guide
Version 1.2 (p. 30).

Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I.
C., Rabin, S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo,
P., Bachelet, D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler,
T., Kaplan, J. O., Kloster, S., Knorr, W., Lasslop, G., \ldots{} Yue, C.
(2016). The status and challenge of global fire modelling.
Biogeosciences, 13(11), 3359--3375.
https://doi.org/10.5194/bg-13-3359-2016

Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., \&
Zelinka, M. (2022). Climate simulations: Recognize the `hot model'
problem. Nature, 605(7908), 26--29.
https://doi.org/10.1038/d41586-022-01192-2

Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B.,
Bouwer, L. M., Braun, A., Colette, A., Deque, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Yiou, P. (2014).
EURO-CORDEX: New high-resolution climate change projections for European
impact research. Regional Environmental Change, 14(2), 563--578.
https://doi.org/10.1007/s10113-013-0499-2

Jain, P., Tye, M. R., Paimazumder, D., \& Flannigan, M. (2020).
Downscaling fire weather extremes from historical and projected climate
models. Climatic Change, 163(1), 189--216.
https://doi.org/10.1007/s10584-020-02865-5

James, G., Witten, D., Hastie, T., \& Tibshirani, R. (2013). An
Introduction to Statistical Learning (Vol. 103). Springer New York.
https://doi.org/10.1007/978-1-4614-7138-7

Jones, M. W., Abatzoglou, J. T., Veraverbeke, S., Andela, N., Lasslop,
G., Forkel, M., Smith, A. J. P., Burton, C., Betts, R. A., van der Werf,
G. R., Sitch, S., Canadell, J. G., Santin, C., Kolden, C., Doerr, S. H.,
\& Le Quere, C. (2022). Global and Regional Trends and Drivers of Fire
Under Climate Change. Reviews of Geophysics, 60(3).
https://doi.org/10.1029/2020RG000726

Joseph, M. B., Rossi, M. W., Mietkiewicz, N. P., Mahood, A. L., Cattau,
M. E., St. Denis, L. A., Nagy, R. C., Iglesias, V., Abatzoglou, J. T.,
\& Balch, J. K. (2019). Spatiotemporal prediction of wildfire size
extremes with Bayesian finite sample maxima. Ecological Applications,
29(6). https://doi.org/10.1002/eap.1898

Krebs, P., Pezzatti, G. B., Mazzoleni, S., Talbot, L. M., \& Conedera,
M. (2010). Fire regime: History and definition of a key concept in
disturbance ecology. Theory in Biosciences, 129(1), 53--69.
https://doi.org/10.1007/s12064-010-0082-z

Krikken, F., Lehner, F., Haustein, K., Drobyshev, I., \& van Oldenborgh,
G. J. (2021). Attribution of the role of climate change in the forest
fires in Sweden 2018. Natural Hazards and Earth System Sciences, 21(7),
2169--2179. https://doi.org/10.5194/nhess-21-2169-2021

Laurent, P., Mouillot, F., Moreno, M. V., Yue, C., \& Ciais, P. (2019).
Varying relationships between \selectlanguage{english}fire radiative power and \selectlanguage{english}fire size at a
global scale. Biogeosciences, 14. https://doi.org/10.5194/bg-16-275-2019

Le Breton, T. D., Lyons, M. B., Nolan, R. H., Penman, T., Williamson, G.
J., \& Ooi, M. K. (2022). Megafire\selectlanguage{english}-induced interval squeeze threatens
vegetation at landscape scales. Frontiers in Ecology and the
Environment, fee.2482. https://doi.org/10.1002/fee.2482

Li, H., Sheffield, J., \& Wood, E. F. (2010). Bias correction of monthly
precipitation and temperature fields from Intergovernmental Panel on
Climate Change AR4 models using equidistant quantile matching. Journal
of Geophysical Research, 115(D10), D10101.
https://doi.org/10.1029/2009JD012882

Liang, S., Hurteau, M. D., \& Westerling, A. L. (2017). Response of
Sierra Nevada forests to projected climate--wildfire interactions.
Global Change Biology, 23(5), 2016--2030.
https://doi.org/10.1111/gcb.13544

Masrur, A., Taylor, A., Harris, L., Barnes, J., \& Petrov, A. (2022).
Topography, Climate and Fire History Regulate Wildfire Activity in the
Alaskan Tundra. Journal of Geophysical Research: Biogeosciences.
https://doi.org/10.1029/2021JG006608

Mauri, A., Girardello, M., Strona, G., Beck, P. S. A., Forzieri, G.,
Caudullo, G., Manca, F., \& Cescatti, A. (2022). EU-Trees4F, a dataset
on the future distribution of European tree species. Scientific Data,
9(1), 37. https://doi.org/10.1038/s41597-022-01128-5

Moreira, F., Ascoli, D., Safford, H., Adams, M. A., Moreno, J. M.,
Pereira, J. M. C., Catry, F. X., Armesto, J., Bond, W., Gonzalez, M. E.,
Curt, T., Koutsias, N., McCaw, L., Price, O., Pausas, J. G., Rigolot,
E., Stephens, S., Tavsanoglu, C., Vallejo, V. R., Fernandes, P. M.
(2020). Wildfire management in Mediterranean-type regions: Paradigm
change needed. Environmental Research Letters, 15(1).
https://doi.org/10.1088/1748-9326/ab541e

Moreno, M. V., \& Chuvieco, E. (2013). Characterising fire regimes in
Spain from fire statistics. International Journal of Wildland Fire,
22(3), 296. https://doi.org/10.1071/WF12061

Morgan, P., Hardy, C. C., Swetnam, T. W., Rollins, M. G., \& Long, D. G.
(2001). Mapping fire regimes across time and space: Understanding coarse
and fine-scale fire patterns. International Journal of Wildland Fire,
10(4), 329. https://doi.org/10.1071/WF01032

Pal, N. R., Bezdek, J. C., \& Hathaway, R. J. (1996). Sequential
Competitive Learning and the Fuzzy c-Means Clustering Algorithms. Neural
Networks, 9(5), 787--796. https://doi.org/10.1016/0893-6080(95)00094-1

Parisien, M.-A., Barber, Q. E., Hirsch, K. G., Stockdale, C. A., Erni,
S., Wang, X., Arseneault, D., \& Parks, S. A. (2020). Fire deficit
increases wildfire risk for many communities in the Canadian boreal
forest. Nature Communications, 11(1), 2121.
https://doi.org/10.1038/s41467-020-15961-y

Pausas, J. G. (2022). Pyrogeography across the western Palaearctic: A
diversity of fire regimes. Global Ecology and Biogeography, geb.13569.
https://doi.org/10.1111/geb.13569

Pausas, J. G., \& Paula, S. (2012). Fuel shapes the fire-climate
relationship: Evidence from Mediterranean ecosystems: Fuel shapes the
fire-climate relationship. Global Ecology and Biogeography, 21(11),
1074--1082. https://doi.org/10.1111/j.1466-8238.2012.00769.x

Pimont, F., Fargeon, H., Opitz, T., Ruffault, J., Barbero, R.,
Martin\selectlanguage{english}-StPaul, N., Rigolot, E., Riviere, M., \& Dupuy, J. (2021).
Prediction of regional wildfire activity in the probabilistic Bayesian
framework of Firelihood. Ecological Applications.
https://doi.org/10.1002/eap.2316

Preisler, H. K., Chen, S.-C., Fujioka, F., Benoit, J. W., \& Westerling,
A. L. (2008). Wildland fire probabilities estimated from weather
model-deduced monthly mean fire danger indices. International Journal of
Wildland Fire, 17(3), 305. https://doi.org/10.1071/WF06162

Resco de Dios, V., Cunill Camprubi, A., Perez-Zanon, N., Pena, J. C.,
Martinez del Castillo, E., Rodrigues, M., Yao, Y., Yebra, M.,
Vega-Garcia, C., \& Boer, M. M. (2022). Convergence in critical fuel
moisture and fire weather thresholds associated with fire activity in
the pyroregions of Mediterranean Europe. Science of The Total
Environment, 806, 151462.
https://doi.org/10.1016/j.scitotenv.2021.151462

Riviere, M., Pimont, F., Delacote, P., Caurla, S., Ruffault, J.,
Lobianco, A., Opitz, T., \& Dupuy, J. L. (2022). A Bioeconomic
Projection of Climate\selectlanguage{english}-Induced Wildfire Risk in the Forest Sector.
Earth's Future, 10(4). https://doi.org/10.1029/2021EF002433

Rodrigues, M., Jimenez-Ruano, A., \& de la Riva, J. (2020). Fire regime
dynamics in mainland Spain. Part 1: Drivers of change. Science of The
Total Environment, 721, 135841.
https://doi.org/10.1016/j.scitotenv.2019.135841

Rodrigues, M., Mariani, M., Russo, A., Salis, M., Galizia, L., \&
Cardil, A. (2021). Spatio\selectlanguage{english}-temporal domains of wildfire\selectlanguage{english}-prone
teleconnection patterns in the Western Mediterranean Basin. Geophysical
Research Letters. https://doi.org/10.1029/2021GL094238

Rogers, B. M., Balch, J. K., Goetz, S. J., Lehmann, C. E. R., \&
Turetsky, M. (2020). Focus on changing fire regimes: Interactions with
climate, ecosystems, and society. Environmental Research Letters, 15(3),
030201. https://doi.org/10.1088/1748-9326/ab6d3a

Ruffault, J., Curt, T., Martin-StPaul, N. K., Moron, V., \& Trigo, R. M.
(2018). Extreme wildfire events are linked to global-change-type
droughts in the northern Mediterranean. Natural Hazards and Earth System
Sciences, 18(3), 847--856. https://doi.org/10.5194/nhess-18-847-2018

Ruffault, J., Curt, T., Moron, V., Trigo, R. M., Mouillot, F., Koutsias,
N., Pimont, F., Martin-StPaul, N., Barbero, R., Dupuy, J.-L., Russo, A.,
\& Belhadj-Khedher, C. (2020). Increased likelihood of heat-induced
large wildfires in the Mediterranean Basin. Scientific Reports, 10(1),
13790. https://doi.org/10.1038/s41598-020-70069-z

Ruffault, J., \& Mouillot, F. (2015). How a new fire-suppression policy
can abruptly reshape the fire-weather relationship. Ecosphere, 6(10),
art199. https://doi.org/10.1890/ES15-00182.1

Son, R., Kim, H., Wang, S.-Y. (Simon), Jeong, J.-H., Woo, S.-H., Jeong,
J.-Y., Lee, B.-D., Kim, S. H., LaPlante, M., Kwon, C.-G., \& Yoon, J.-H.
(2021). Changes in fire weather climatology under 1.5 degC and 2.0 degC
warming. Environmental Research Letters, 16(3), 034058.
https://doi.org/10.1088/1748-9326/abe675

Taylor, S. W. (2020). Atmospheric Cascades Shape Wildfire Activity and
Fire Management Decision Spaces Across Scales \selectlanguage{english}- A Conceptual Framework
for Fire Prediction. Frontiers in Environmental Science, 8, 527278.
https://doi.org/10.3389/fenvs.2020.527278

Turco, M., Jerez, S., Augusto, S., Tarin-Carrasco, P., Ratola, N.,
Jimenez-Guerrero, P., \& Trigo, R. M. (2019). Climate drivers of the
2017 devastating fires in Portugal. Scientific Reports, 9(1), 13886.
https://doi.org/10.1038/s41598-019-50281-2

Turco, M., Rosa-Canovas, J. J., Bedia, J., Jerez, S., Montavez, J. P.,
Llasat, M. C., \& Provenzale, A. (2018). Exacerbated fires in
Mediterranean Europe due to anthropogenic warming projected with
non-stationary climate-fire models. Nature Communications, 9(1), 3821.
https://doi.org/10.1038/s41467-018-06358-z

Turco, M., von Hardenberg, J., AghaKouchak, A., Llasat, M. C.,
Provenzale, A., \& Trigo, R. M. (2017). On the key role of droughts in
the dynamics of summer fires in Mediterranean Europe. Scientific
Reports, 7(1), 81. https://doi.org/10.1038/s41598-017-00116-9

Van Wagner, C. E. (1987). Development and structure of the canadian
forest fireweather index system (p. 35) {[}Forestry Technical Report
35{]}. Canadian Forestry Servic,.

Vilar, L., Herrera, S., Tafur-Garcia, E., Yebra, M., Martinez-Vega, J.,
Echavarria, P., \& Martin, M. P. (2021). Modelling wildfire occurrence
at regional scale from land use/cover and climate change scenarios.
Environmental Modelling \& Software, 145, 105200.
https://doi.org/10.1016/j.envsoft.2021.105200

Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R.,
San-Miguel-Ayanz, J., Liberta, G., \& Krzeminski, B. (2020). ERA5-based
global meteorological wildfire danger maps. Scientific Data, 7(1), 216.
https://doi.org/10.1038/s41597-020-0554-z

Wood, S. N., Pya, N., \& Safken, B. (2016). Smoothing Parameter and
Model Selection for General Smooth Models. Journal of the American
Statistical Association, 111(516), 1548--1563.
https://doi.org/10.1080/01621459.2016.1180986

Woolford, D. G., Martell, D. L., McFayden, C. B., Evens, J., Stacey, A.,
Wotton, B. M., \& Boychuk, D. (2021). The development and implementation
of a human-caused wildland fire occurrence prediction system for the
province of Ontario, Canada. Canadian Journal of Forest Research, 51(2),
303--325. https://doi.org/10.1139/cjfr-2020-0313

Wooster, M. J., Roberts, G. J., Giglio, L., Roy, D., Freeborn, P.,
Boschetti, L., Justice, C., Ichoku, C., Schroeder, W., Davies, D.,
Smith, A., Setzer, A., Csiszar, I., Strydom, T., Frost, P., Zhang, T.,
Xu, W., de Jong, M., Johnston, J. San-Miguel, J. (2021). Satellite
remote sensing of active fires: History and current status, applications
and future requirements. Remote Sensing of Environment, 267, 112694.
https://doi.org/10.1016/j.rse.2021.112694

Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., \& Yang,
H. (2021). Increasing forest fire emissions despite the decline in
global burned area. Science Advances, 7(39), eabh2646.
https://doi.org/10.1126/sciadv.abh2646

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