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\DOI{10.5802/crgeos.263}
\datereceived{2023-10-06}
\datererevised{2024-05-27}
\dateaccepted{2024-05-27}
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\dateposted{2024-06-18}
\begin{document}

\begin{noXML}

\CDRsetmeta{articletype}{research-article}

\shortrunauthors

\title{K\"{o}ppen--Geiger climate classification across France based on
an ensemble of high-resolution climate projections}

\alttitle{Classification climatique de K\"{o}ppen--Geiger en France sur
la base d'un ensemble de projections climatiques \`{a} haute
r\'{e}solution}

\author{\firstname{Laurent} \lastname{Strohmenger}\CDRorcid{0000-0003-4315-0968}\IsCorresp}
\address{Universit\'{e} Paris-Saclay, 
INRAE, HYCAR Research Unit, Antony, France} 
\email[L. Strohmenger]{Laurent.strohmenger@inrae.fr}

\author{\firstname{Lila} \lastname{Collet}\CDRorcid{0000-0003-2569-1509}}
\addressSameAs{1}{Universit\'{e} Paris-Saclay, 
INRAE, HYCAR Research Unit, Antony, France} 
\address{OSIRIS Department, 
EDF Research and Development Division, 
EDF Lab Paris-Saclay, Palaiseau, France} 

\author{\firstname{Vazken} \lastname{Andr\'{e}assian}\CDRorcid{0000-0001-7124-9303}}
\addressSameAs{1}{Universit\'{e} Paris-Saclay, 
INRAE, HYCAR Research Unit, Antony, France} 

\author{\firstname{Lola} \lastname{Corre}}
\address{CNRM, Universit\'{e} de Toulouse, 
M\'{e}t\'{e}o-France, CNRS, Toulouse, France}

\author{\firstname{Fabienne} \lastname{Rousset}}
\address{Direction de la Climatologie et des Services Climatiques, 
M\'{e}t\'{e}o-France, Toulouse, France} 

\author{\firstname{Guillaume} \lastname{Thirel}\CDRorcid{0000-0002-1444-1830}\IsCorresp}
\addressSameAs{1}{Universit\'{e} Paris-Saclay, 
INRAE, HYCAR Research Unit, Antony, France} 
\addressSameAs{3}{CNRM, Universit\'{e} de Toulouse, 
M\'{e}t\'{e}o-France, CNRS, Toulouse, France}
\email[G. Thirel]{Guillaume.thirel@inrae.fr}

\keywords{\kwd{Climate change}
\kwd{Climate classification}
\kwd{Temperature}
\kwd{Precipitation}
\kwd{K\"{o}ppen--Geiger}
\kwd{Climate maps}
\kwd{Mediterranean climate}}

\altkeywords{\kwd{Changement climatique}
\kwd{Classification climatique}
\kwd{Temp\'{e}rature}
\kwd{Pr\'{e}cipitations}
\kwd{K\"{o}ppen--Geiger}
\kwd{Cartes climatiques}
\kwd{Climat m\'{e}diterran\'{e}en}}

\thanks{French Ministry for Ecology (contract no.~2103295967)}

\begin{abstract} 
Climate change will define new living and environmental conditions in
many areas in the world, deeply affecting our societies. Public
outreach on the impacts of climate change on societies and ecosystems
is difficult, yet crucial for fostering mitigation and adaptation
strategies. To quantify the climate change and communicate about it in
easily-understandable terms, we used the climate classification system
of K\"{o}ppen--Geiger. 84 climate projections bias corrected with two
methods were used to calculate K\"{o}ppen--Geiger climate types at an
8-km resolution for 30-year periods from 1976 to 2099 in France.
Moderate changes in climate types are expected in 2035 for
approximately 20\% of France, regardless of the emission scenario.
However, major changes are expected after 2040 under RCP 8.5 for 86\%
of France, with a massive expansion of the temperate, hot summer with
or without dry season, as well as the expansion of temperate climate in
mountainous regions. We found that these changes could reach an average
transition speed of 7~{km}\textperiodcentered{yr}\textsuperscript{-1} northwards.
These rapid changes will have unprecedented consequences on
agriculture, biodiversity, water and energy management, not to mention
on societies. Similar changes can be expected in other European
countries that share the diverse climatic and topographical conditions
found in France.
\end{abstract}

\begin{altabstract} 
Le changement climatique se traduira par de nouvelles conditions de vie
qui affecteront profond\'{e}ment nos soci\'{e}t\'{e}s. Communiquer les
impacts du changement climatique \`{a} un large public est difficile,
mais pourtant essentiel pour encourager les strat\'{e}gies
d'att\'{e}nuation et d'adaptation. La repr\'{e}sentation du changement
climatique peut \^{e}tre simplifi\'{e}e au moyen de de cartes de types
de climat telles que la classification propos\'{e}e par
K\"{o}ppen--Geiger. Nous avons utilis\'{e} la classification de
K\"{o}ppen--Geiger pour d\'{e}terminer les types de climat en France
sur des p\'{e}riodes de 30 ans entre 1976 et 2099 \`{a} l'aide de
donn\'{e}es issues d'un ensemble de 84 projections climatiques
corrig\'{e}es par deux m\'{e}thodes de correction de biais \`{a} une
r\'{e}solution de 8 km. Les changements de types de climat resteront
mod\'{e}r\'{e}s d'ici 2035, ils concerneront 20 \% de la France, quel
que soit le sc\'{e}nario d'\'{e}mission. Cependant, les changements
seront majeurs apr\`{e}s 2040 sous RCP 8.5, ils concerneront 86 \% de
la France, et se traduiront par une expansion du climat temp\'{e}r\'{e}
avec \'{e}t\'{e} chaud (temp\'{e}rature moyenne
${\geq}$ 22~\text{\textdegree}C) avec ou sans saison s\`{e}che, ainsi
que par l'expansion du type de climat temp\'{e}r\'{e} dans les
r\'{e}gions montagneuses. Ces transitions de types de climat
progresseront du sud vers le nord du pays \`{a} une vitesse moyenne
estim\'{e}e \`{a} 7~{km}\textperiodcentered{yr}\textsuperscript{-1}. Ces
transitions rapides de type de climat auront des cons\'{e}quences sans
pr\'{e}c\'{e}dent sur l'agriculture, la biodiversit\'{e}, la gestion de
l'eau, l'\'{e}nergie, et nos soci\'{e}t\'{e}s. On peut anticiper des
changements similaires dans d'autres pays europ\'{e}ens dont les
conditions climatiques et topographiques sont similaires \`{a} celles
que l'on peut observer en France.
\end{altabstract} 

\maketitle

\twocolumngrid

\end{noXML}

\section{Introduction}\label{sec1}
Climate is the main driver of many components of natural and
anthropogenic systems. Societies~and ecosystems have adapted to
long-term averages of precipitation and air temperature that have
remained relatively steady over multi-year scales and can be
categorized by climate types. However, the climate has been changing
rapidly since industrialization, and future changes are expected to
intensify by the end of the  21st century  \citep{Portneretal2022},
with potential consequences on water resources 
\citep{Arnell1999,Donnellyetal2017,Hagemannetal2013},  biodiversity 
\citep{Chenetal2011,ParmesanYohe2003,RomanPalaciosWiens2020,TrewMaclean2021}, 
forest  \citep{LancasterHumphreys2020,VajdaVenalainen2005,Xuetal2020},
agriculture  \citep{Bhattacharya2019,Ceglaretal2019,Fragaetal2020}, or
the energy sector  \citep{Ahmad2021,Linnerudetal2011}.

While the Intergovernmental Panel on Climate Change (IPCC) recently
released its sixth report 
\citep{MassonDelmotteetal2021,Portneretal2022} since 1990, gathering
common scientific knowledge, conclusions, and breakthroughs regarding
climate change issues, significant challenges remain in spreading this
knowledge to the general public and decision-makers 
\citep{Pergaetal2023}. Many attempts have been made recently to improve
communication on climate change impact, starting with the IPCC itself,
which produces shorter and more readable summaries for policymakers and
now works with artists to illustrate their main messages  (see, e.g.,
front page of the IPCC special report 
\citep{IntergovernmentalPanelonClimateChange2018} on Global Warming
of  1.5~\textdegree C).

In France, as in many countries, impact studies have been conducted at
regional or national scales  \citep{Bonnetetal2020,Chauveauetal2013},
leading to scientific reports, papers, and adaptation plans 
(e.g., the
French national plan for climate change adaptation 
\citep{MinisteredelaTransitionEcologiqueetSolidaire2018}). However,
this prolific scientific literature remains mainly among experts and
has not yet become common knowledge  \citep{Pergaetal2023}, although
there are several initiatives ongoing in France that aim to inject the
most-up-to-date scientific knowledge in support of decisions made by
socio-economic actors and public administrations (see the French
Regional Groups of Climate Experts (GRECs) network,
\url{https://www.cnfcg.fr/index.php/grec-en}). We thus need more
efforts to produce data visualization tools for communicating the
impacts of climate change  \citep{Pergaetal2023}, and maps of climate
types can be extremely useful to this end, proving to be simple yet
striking communication tools  \citep{Oliver1991}.

Climate type maps can be produced based on a typology of climates such
as the well-known K\"{o}ppen--Geiger classification  \citep{Koppen1936}
that reflects the different climate conditions that shape landscape
diversity. This classification system describes the main climate
classes as well as the precipitation and temperature regimes using a
three-level label. The K\"{o}ppen--Geiger classification has been
applied using precipitation and air temperature data at a large scale 
\citep{Ackerman1941,Becketal2018,Becketal2023,Carvalhoetal2020,Cuietal2021,Peeletal2007},
but rarely at the local scale  \citep{Dubreuil2022,Evenoetal2016}. This
means that coarse data, poorly relevant at a local scale, are generally
used in such analyses. Results are hence sometimes debatable when
analyzing these outputs at the national scale. The case of the French
Pyrenees is a clear illustration of this issue, where the poor
representation of the mountain range topography results in an
east--west climate disparity that is not reproduced by global climate
models and in large biases in impact analysis  \citep{Pageetal2008}.

Projections of future climate types based on the Coupled Model
Intercomparison Project Phase 5  (CMIP5) database  exist 
\citep{Becketal2018,Becketal2023}, although the authors of this study
stressed that a simple ``anomaly method'' was applied to account for
the climate model signal at the global scale. This method accounts for
the change in the mean signal only and bypasses finer temporal changes,
such as longer drought periods in summer and more intense rainy
seasons. This methodological choice can be explained by the difficulty
in applying bias correction or downscaling methods at the global scale,
since these can be computationally expensive and extremely challenging.
Statistical bias correction methods based on quantile mapping 
\citep{Michelangelietal2009,Verfaillieetal2017} adjust the whole
distribution of raw climate projections based on local observations and
result in a more precise representation of local climate variability.
These approaches, which are easier to set up and require lower
computation costs, can be applied at the country scale to accurately
inform local impact studies.

In this article, we use tailored high-resolution and bias-corrected
climate projections to assess local changes in K\"{o}ppen--Geiger
climate types across France by the end of the  21st century. The
climate dataset  \citep{Soubeyrouxetal2021} is based on 20 global
climate model (GCM)/regional climate model (RCM) couples from the
EURO-CORDEX 12-km regional projections driven by CMIP5, covering three
emissions scenarios, namely, representative concentration pathways
(RCP) 2.6, 4.5, and 8.5. The data have been projected on an 8-km
grid and \mbox{bias-corrected}
with the ADAMONT  \citep{Verfaillieetal2017}
and \mbox{CDF-t} \citep{Michelangelietal2009} methods, using the SAFRAN
reanalysis  \citep{QuintanaSeguietal2008,Vidaletal2010} as the
observational reference dataset. We first calculated the observed
climate types and compared them with those produced with climate model
outputs over the historical period (1976--2005). Then, we explored how
climate types are expected to change both temporally and spatially for
each emission scenario across the climate model ensemble. In total, we
used a set of 84 climate projections to calculate climate types across
France over a 30-year sliding window from 1976 to 2099.

\section{Methods}\label{sec2}

\subsection{Climate projections}\label{sec2.1}
We used air temperature and precipitation data from a selection of bias
corrected CMIP5-based EURO-Cordex climate projections 
\citep{Jacobetal2020} provided by the DRIAS portal 
\citep{Soubeyrouxetal2021}. These climate projections are based on
three greenhouse gas emissions scenarios that reflect different levels
of human pressure on the climate. RCP 2.6 is the scenario with the
lowest greenhouse gas emissions and a global warming below 
2~\textdegree C at the end of the century. RCP~8.5 is the scenario
without regulation of greenhouse gas emissions and a global warming of 
5~\textdegree C at the end of the century. RCP 4.5 lies between RCP 2.5
and 8.5, with greenhouse gas emissions stabilizing over the next decade
and global warming of approximately  3~\textdegree C by the end of the
century. These RCPs have been used as forcing data for GCM coupled to
RCM in order to account for uncertainty in the future evolution of
society and several GCMs and RCMs have been used to account for climate
modeling uncertainty. Time series of air temperature and precipitation
from RCMs were bias-corrected based on the fine-scale SAFRAN reanalysis
\citep{QuintanaSeguietal2008,Vidaletal2010} using two statistical
methods, namely, ADAMONT  \citep{Verfaillieetal2017} and \mbox{CDF-t} 
\citep{Michelangelietal2009}, based on the quantile--quantile mapping
approach. Regarding ADAMONT, the corrections are applied by season and
by weather regime  \citep{Verfaillieetal2017}, while the \mbox{CDF-t} method
is applied on a monthly basis  \citep{Michelangelietal2009}. Combining
the RCPs, GCMs, RCMs, and bias correction methods results in a total of
84 climate projections, including 22, 22, and 40 projections for RCPs
2.6, 4.5, and 8.5, respectively  (Table~\ref{tab1}). The projected
times series of temperature and precipitation are available on an 8-km
grid in France, at a daily time step from 1976 to 2099. 

\begin{table*}
\caption{\label{tab1}Combinations of all the GCM, RCM, and RCP
scenarios used in the present study}
\begin{tabular}{ccccc} 
\thead
GCM & RCM & RCP 2.6 & RCP 4.5 & RCP 8.5 \\ 
\endthead
CNRM-CM5-LR & ALADIN63 & \ding{51} & \ding{51} & \ding{51} \\ 
CNRM-CM5-LR & HadREM3-GA7-05 &  &  & \ding{51} \\ 
CNRM-CM5-LR & RACMO22E & \ding{51} & \ding{51} & \ding{51} \\ 
EC-EARTH & HadREM3-GA7-05 & \ding{51} &  & \ding{51} \\ 
EC-EARTH & HIRHAM5 &  &  & \ding{51} \\ 
EC-EARTH & RACMO22E & \ding{51} & \ding{51} & \ding{51} \\ 
EC-EARTH & RCA4 & \ding{51} & \ding{51} & \ding{51} \\ 
HadGEM2-ES & ALADIN63 &  &  & \ding{51} \\ 
HadGEM2-ES & CCLM4-8-17 &  & \ding{51} & \ding{51} \\ 
HadGEM2-ES & HadREM3-GA7-05 & \ding{51} &  & \ding{51} \\ 
HadGEM2-ES & RegCM4-6 & \ding{51} &  & \ding{51} \\ 
IPSL-CM5A-MR & HIRHAM5 &  &  & \ding{51} \\ 
IPSL-CM5A-MR & RCA4 &  & \ding{51} & \ding{51} \\ 
IPSL-CM5A-MR & WRF381P &  & \ding{51} & \ding{51} \\ 
MPI-ESM-LR & CCLM4-8-17 & \ding{51} & \ding{51} & \ding{51} \\ 
MPI-ESM-LR & RegCM4-6 & \ding{51} &  & \ding{51} \\ 
MPI-ESM-LR & REMO2009 & \ding{51} & \ding{51} & \ding{51} \\ 
NorESM1-M & HIRHAM5 &  & \ding{51} & \ding{51} \\ 
NorESM1-M & REMO2015 & \ding{51} & \ding{51} & \ding{51} \\ 
NorESM1-M & WRF381P &  &  & \ding{51}
\botline
\end{tabular}
\tabnote{All the combinations were adjusted
with the ADAMONT and \mbox{CDF-t} methods and all have simulations over the
1976--2005 historical period.}
\vspace*{5pt}
\end{table*}

\subsection{Climate classification}\label{sec2.2}
The K\"{o}ppen--Geiger climate classification 
\citep{Becketal2018,Koppen1936,Peeletal2007} uses mean annual or
monthly air temperature and precipitation amounts that are compared
with the related thresholds during summer and winter seasons to
describe climate conditions on three levels  (Table~\ref{tab2}). The
first level (letters A--E) expresses the main classes within tropical
(A), arid (B), temperate (C), cold (D), and polar (E) climates. The
second letter expresses the precipitation regime, such as dry summer
(s), or no dry season (f). The third letter expresses the air
temperature regime, such as hot (a), warm (b), or cold (d) summer. We
computed the K\"{o}ppen--Geiger climate types over 30-year periods from
1976--2005 to 2070--2099, for 8568 grid points at an 8-km resolution in
France, and for each of the 84 projections of future climate, as well
as for the historical reference computed with data from the French
SAFRAN reanalysis  \citep{Vidaletal2010}. For each grid point and
30-year period, we retained the most frequent climate type (among all
the available climate projections) as the dominant one to produce maps
and for statistics.

\begin{table*}
\caption{\label{tab2}Precipitation and temperature criteria of the
K\"{o}ppen--Geiger climate classification according to 
\citet{Peeletal2007}}
\tabcolsep=8pt
\begin{tabular}{cccccc}  
\thead
1st & 2nd & 3rd & Description & Criteria & Relevant for France \\ 
\endthead
A & ~ & ~ & Tropical & 
$T_{\mathrm{cold}}${$^{\mathrm{a}}$}\ $\geq18$ &  \\ 
~ & f & ~ & - Rainforest & 
$P_{\mathrm{dry}}${$^{\mathrm{b}}$}\ $\geq60$ &  \\ 
~ & m & ~ & - Monsoon & Not $\mathrm{A}_{\mathrm{f}}$ and  
$P_{\mathrm{dry}} \geq$\ 100 - MAP$^{\mathrm{c}}$/25 &  \\ 
~ & w & ~ & - Savannah & Not $\mathrm{A}_{\mathrm{f}}$ and  
$P_{\mathrm{dry}}$ $<$ 100 - MAP/25\vspace*{3pt} &  \\ 
B & ~ & ~ & Arid & MAP $< 10 * P_{\mathrm{threshold}}${$^{\mathrm{d}}$} &  \\ 
~ & W & ~ & - Desert & MAP $< 5 * P_{\mathrm{threshold}}$ &  \\ 
~ & S & ~ & - Steppe & MAP $\geq 5 * P_{\mathrm{threshold}}$ &  \\ 
~ & ~ & h & -- Hot & MAT$^{\mathrm{e}}$\ ${\geq}$\ 18 &  \\ 
~ & ~ & k & -- Cold & MAT $<$ 18\vspace*{3pt} &  \\ 
C & ~ & ~ & Temperate & $T_{\mathrm{hot}}${$^{\mathrm{f}}$}\ $>10$ and 
$0<T_{\mathrm{cold}}${$^{\mathrm{g}}$}\ $<18$ & Yes \\ 
~ & s & ~ & - Dry summer & 
$P_{\mathrm{sdry}}${$^{\mathrm{h}}$}\ $<40$ and 
$P_{\mathrm{sdry}}<P_{\mathrm{wwet}}${$^{\mathrm{i}}$}/3 & Yes \\ 
~ & w & ~ & - Dry winter & 
$P_{\mathrm{wdry}}${$^{\mathrm{j}}$}\ $<P_{\mathrm{swet}}${$^{\mathrm{k}}$}/10 &  \\ 
~ & f & ~ & - Without dry season & Not $\mathrm{C}_{\mathrm{s}}$ and 
Not $\mathrm{C}_{\mathrm{w}}$ & Yes \\ 
~ & ~ & a & -- Hot summer & $T_{\mathrm{hot}}\geq22$ & Yes \\ 
~ & ~ & b & -- Warm summer & Not $\mathrm{C}_{\mathrm{a}}$ and 
$T_{\mathrm{mon}}10${$^{\mathrm{l}}$}\ $\geq4$  & Yes \\ 
~ & ~ & c & -- Cold summer  & Not ($\mathrm{C}_{\mathrm{a}}$ or 
$\mathrm{C}_{\mathrm{b}}$) and $1\leq T_{\mathrm{mon}}10<4$ & Yes\vspace*{3pt} \\ 
D & ~ & ~ & Cold & 
$T_{\mathrm{hot}}>10$ and
$T_{\mathrm{cold}}\leq0$ & Yes \\ 
~ & s & ~ & - Dry summer & 
$P_{\mathrm{sdry}}<40$ and
$P_{\mathrm{sdry}}<P_{\mathrm{wwet}}/3$ &  \\ 
~ & w & ~ & - Dry winter  & 
$P_{\mathrm{wdry}}<P_{\mathrm{swet}}/10$ &  \\ 
~ & f & ~ & - Without dry season & Not $\mathrm{D}_{\mathrm{s}}$ and 
not $\mathrm{D}_{\mathrm{w}}$ & Yes \\ 
~ & ~ & a & -- Hot summer & 
$T_{\mathrm{hot}}\geq22$ &  \\ 
~ & ~ & b & -- Warm summer & Not $\mathrm{D}_{\mathrm{a}}$ and 
$T_{\mathrm{mon}}10 \geq 4$ & Yes \\ 
~ & ~ & c & -- Cold summer & Not $\mathrm{D}_{\mathrm{a}}$, not 
$\mathrm{D}_{\mathrm{b}}$, and not $\mathrm{D}_{\mathrm{d}}$ & Yes \\ 
~ & ~ & d & -- Very cold winter & Not 
$\mathrm{D}_{\mathrm{a}}$ and not $\mathrm{D}_{\mathrm{b}}$ and 
$T_{\mathrm{cold}}<-38$\vspace*{3pt} &  \\ 
E & ~ & ~ & Polar  & 
$T_{\mathrm{hot}}<10$ & Yes \\ 
~ & T & ~ & - Tundra & 
$T_{\mathrm{hot}}>0$ & Yes \\ 
~ & F & ~ & - Frost & 
$T_{\mathrm{hot}}\leq0$ &
\botline 
\end{tabular}
\tabnote{$^{\mathrm{a}}T_{\mathrm{cold}}$\ $=$\ monthly temperature of
the coldest month; 
$^{\mathrm{b}}P_{\mathrm{dry}}$\ $=$\ monthly precipitation of the
driest month; 
$^{\mathrm{c}}$MAP $=$ mean annual precipitation;
$^{\mathrm{d}}$if precipitation during winter ${>}$ 0.7 $*$ MAP, then
$P_{\mathrm{threshold}}$\ $=$\ 2 $*$ MAT, else 
$P_{\mathrm{threshold}}$\ $=$\ 2 $*$ MAT $+$ 28;
$^{\mathrm{e}}$MAT $=$ mean annual temperature;  
$^{\mathrm{f}}T_{\mathrm{hot}}$\ $=$\ monthly temperature of the
hottest month; 
$^{\mathrm{g}}T_{\mathrm{cold}}$\ $=$\ monthly temperature of the
coldest month;
$^{\mathrm{h}}P_{\mathrm{sdry}}$\ $=$\ monthly precipitation of the
driest month in summer; 
$^{\mathrm{i}}P_{\mathrm{wwet}}$\ $=$\ monthly precipitation of the
wettest month in winter; 
$^{\mathrm{j}}P_{\mathrm{wwet}}$\ $=$\ monthly precipitation of the
wettest month in winter; 
$^{\mathrm{k}}P_{\mathrm{swet}}$\ $=$\ monthly precipitation of the
wettest month in summer; 
$^{\mathrm{l}}T_{\mathrm{mon}}10$\ $=$\ number of months with
temperature $>$ 10~\textdegree C.} 
\end{table*}

\section{Results}\label{sec3}

\subsection{Climate models reproduce historical climate types
well}\label{sec3.1}
Observed climate types computed with the SAFRAN reanalysis across
France for the period 1976--2005  (Figure~\ref{fig1}b) show a large
proportion of land under a temperate climate, with 93\% of France
associated with a first level of the K\"{o}ppen--Geiger classification
in the category temperate (C), followed by 6.2\% in the category cold
(D), and 0.8\% in the category polar (E) climate. Temperate climate
grid points stand in the low-elevation areas. They are distributed
among temperate climates without a dry season and warm summer (Cfb) for
85\% of France, except for the Mediterranean region and the island of
Corsica (southeast France), where temperate climates with a dry season
and a hot (Csa, 4.8\%) and warm (Csb, 2.4\%) summer are located in low
and high elevations, respectively. A few grid points show a temperate
climate without a dry season and hot summer (Cfa, 0.6\%) at the
transition zone between Csa and Csb in southeast France, in
low-elevation areas between the Pyrenees, Massif Central, and Alps
mountain ranges (see  Figure~\ref{fig1}a for the main mountainous areas
and regions of France mentioned here). The cold climate grid points are
located in mid-mountainous regions such as the Vosges (northeast
France), Jura (mid-east), Alps (southeast), Massif Central (mid-south),
and the Pyrenees (southwest). They are distributed among areas of cold
climate without a dry season and a warm summer (Dfb) for 4.5\% of
France, mostly located in the low-elevation mountainous areas, while
areas of cold climate without a dry season and cold summer (Dfc, 1.6\%)
stand in middle-elevation mountainous areas. One grid point of cold
climate with a dry and warm summer (Dsb, 0.01\%) is located at a
high-elevation grid point on Corsica. The polar climate grid points
consist of tundra (ET, 0.8\%) located in high-elevation areas of the
Alps and\unskip\break 
Pyrenees.

\begin{figure*}
\includegraphics{fig01}
\caption{\label{fig1}Maps of (a)~elevation and French regions 
mentioned in the text, (b)~the K\"{o}ppen--Geiger climate types
computed with SAFRAN, and (c)~the most frequent climate type computed
with the climate model outputs across France over the historical time
period (1976--2005). The red squares highlight differences in climate
types between those computed with SAFRAN and the most frequent one
computed with climate model outputs.}
\vspace*{4pt}
\end{figure*}

The most frequent climate types computed with climate model outputs for
the historical time period (1976--2005) are in agreement with those
computed with the SAFRAN data  (Figure~\ref{fig1}c). Indeed, the most
frequent climate type computed with the climate model outputs
corresponds with the type computed with the SAFRAN data for 99.24\% of
the grid points. A further analysis showed that the remaining 0.76\% of
grid points are divided into 0.70\% for which the second most frequent
simulated climate type matches the observed one, and only 0.06\% for
which neither the first nor the second most frequent simulated climate
types match the observed one. Disagreements are observed for grid
points\unskip\break 
located at transition zones between two climate types, and in
steep regions such as the Alps and Pyrenees  (Figure~\ref{fig1}). The
agreement rates between climate models (i.e., the proportion of climate
models that\unskip\break 
predict the climate type most frequently simulated) are
very high for the historical time period. Among all grid points, the
mean agreement rate is 95\% (minimum and maximum of 46\% and 100\%, 
Figure~\ref{fig3}) with most of the grid points (90\%) showing
agreement rates higher than~83\%.

Overall, these results show the good ability of climate modeling chains
to simulate the present climate over France, which was expected given
the statistical processing applied in this study. This lends confidence
to the use of these simulations in our study.

\subsection{Future climate types diverge with RCPs\newline 
starting from
mid-century}\label{sec3.2}
In the near future (2020--2049), moderate changes in climate types are
projected regardless of the greenhouse gas emissions scenario (17--20\%
of grid points, see  Figure~\ref{fig2} and 
Supplementary Figure~S1) compared to the
historical 1976--2005 period. 

\begin{figure*}
\includegraphics{fig02}
\caption{\label{fig2}Maps of the K\"{o}ppen--Geiger climate types
computed with climate projection data for France during the historical
period (1976--2005), the near future (2020--2049), and the far future
(2070--2099) for RCPs 2.6, 4.5, and 8.5. The colors refer to the most
frequent climate type computed among all climate projections.}
\end{figure*}

\begin{figure*}
\includegraphics{fig03}
\caption{\label{fig3}Agreement rates (i.e., the proportion of climate
models that predict the climate type most frequently simulated, see
Figure~\ref{fig2}) for France during the historical period
(1976--2005), and in the near-future (2020--2049), and far-future
(2070--2099) periods, for RCPs 2.6, 4.5, and 8.5.} 
\end{figure*}

Changes in the near future are mainly constrained to the
mid-mountainous regions, the south, and the western France 
(Figure~\ref{fig2}). Low-altitude regions in southwest and southeast
France show major changes with approximately 9\% of the grid points
shifting from a temperate climate without a dry season and warm (Cfb)
to hot (Cfa) summer  (Figure~\ref{fig2} and
Supplementary Figure~S1). The Vosges, Jura,
Massif central, and Alps mid-mountainous regions show a transition from
a cold to a temperate climate without a dry season and warm summer
(from Dfb to Cfb for 5\% of France). Western France also shows local
shifts from a temperate climate without a dry season and warm summer
(Cfb) to a dry season (Csb). 

\begin{figure*}
\includegraphics{fig04}
\caption{\label{fig4}Percentage of grid points with most frequent
climate types among climate projections and their transitions from the
historical (1976--2005) to the far-future period (2070--2099) for RCPs
2.6, 4.5, and 8.5. Percentage values are calculated on the total
surface area of France, all types of climate considered.}
{\vspace*{-1pt}}
\end{figure*}

However, in the far future (2070--2099), the spatial distribution of
climate types shows diverging trajectories depending on the emissions
scenario with a breakpoint around 2040 (Figure~\ref{fig5} and
Supplementary Figure~S1). This leads to 18\%, 42\%, and 86\% of the
grid points showing changes in climate type by 2070--2099 for RCPs 2.6,
4.5, and 8.5, respectively. In the far future and under the
high-emissions\unskip\break 
scenario (RCP 8.5), the climate types remain unchanged
for  a very small number of grid points located in the north (Cfb) and
around the Mediterranean coast (Csa). Compared with the historical
period, the proportion of grid points with a temperate climate without
a dry season and warm summer (Cfb) decreases from 85\% to 14\% of
France  (Figures~\ref{fig2}, \ref{fig4}, and~\ref{fig5}). The Cfb
climate type changes to mostly a temperate climate and a hot summer
without a dry season (Cfa, 43\%) northeast, and with a dry season (Csa,
29\%) in the west of France  (Figures~\ref{fig2} and~\ref{fig4}). In
the mountainous regions, almost all grid points with a cold climate
without a dry season and warm (Dfb) or cold (Dfc) summer change to a
temperate climate (Csb), and all grid points with a polar climate (ET)
change to a cold climate Dfc and Dfb  (Figures~\ref{fig2}
and~\ref{fig4}). Brittany, located in northwestern France, would see a
shift from a temperate climate with a warm summer and no dry season
(Cfb) to a temperate climate with a dry and warm summer (Csb) and with
a dry and hot summer (Csa), the latter being typical of the
Mediterranean climate in the historical period  (Figure~\ref{fig2}).
Based on the climate projections, a new---for France---climate type
could also be expected in southeast France where a grid point showing a
temperate climate with a dry and hot 
summer (Csa) would shift to an
arid steppe hot climate (BSh,  Figure~\ref{fig2} 
and Supplementary\unskip\break
Figure~S2). 

\begin{figure*}
\includegraphics{fig05}
\caption{\label{fig5}Proportion of France covered by the most frequent
climate types (among climate projections) for 30-year rolling periods
from 1976 to 2099 for RCPs 2.6, 4.5, and 8.5. The vertical gray lines
indicate the center of the historical, the near-future, and the
far-future periods, respectively.}
{\vspace*{2pt}}
\end{figure*}

The agreement rates between projections of the most frequent climate
type computed with the climate model outputs decrease in the future and
for the high-emissions scenario, compared to the agreement rates of the
historical period (Figure~\ref{fig3}). For the 2070--2099 period, the
mean (quantiles 10th and 90th) agreement rates are 88\% (64--100\%),
71\% (45--91\%), and 65\% (48--85\%) for RCPs 2.6, 4.5, and 8.5,
respectively. The lower agreement rates are essentially observed in
transition zones between two climate types (Figures~\ref{fig2}
and~\ref{fig3}).

\subsection{Changes in climate type: towards a drier and warmer
climate}\label{sec3.3}
The transitions in climate types share the same spatiotemporal patterns
from 1980 to 2040, regardless of the emissions scenario 
(Figure~\ref{fig6}). However, after 2040, the speed and extent of
transitions from one type to another depend on the emissions scenario,
with the higher and lower spread observed for the high- and
low-emissions scenarios, respectively  (Figures~\ref{fig5}
and~\ref{fig6}). The intermediate-emissions scenario (RCP 4.5) shows
intermediate spatial and temporal patterns of climate transitions. 

\begin{figure*}
\includegraphics{fig06}
\caption{\label{fig6}Spatial and temporal changes of climate type
transition of the most frequent climate types (among climate
projections) in France for RCPs 2.6, 4.5, and 8.5. The first and second
labels in the column headers refer to the climate type before and after
the transition, respectively, while the color bar refers to the year of
transition.}
\end{figure*}

The low-altitude areas between the Massif central and the Pyrenees
mountains and the Alps in southeast France show a transition from a
temperate climate without a dry season and a warm to hot summer (from
Cfb to Cfa) that starts around 2000  (Figure~\ref{fig6} and
Supplementary Figure~S3). For
the low-emission scenarios, the Cfb--Cfa transition spreads slowly
northward, while remaining spatially limited to southwest and southeast
France. However, for RCPs 4.5 and 8.5, this transition shows a wider
spread up to the center of France by 2050 and to the north and east of
France, as well as in the mid-mountainous regions by 2080. This climate
transition is expected to spread northward at a mean velocity of
approximately  $7~\mathrm{km}{\cdot}\mathrm{yr}^{-1}$ for the
high-emissions scenario (700~km in ca.~100 years,  Figure~\ref{fig6}).
Some of these areas might even change a second time from no dry season
to a dry summer (from Cfa to Csa) in the west and south of France after
2070 for the high-emission scenario  (Figure~\ref{fig6} and
Supplementary\unskip\break 
Figure~S3). 

The west of France would transition from a temperate climate without a
dry season and warm summer to a temperate climate with a dry and hot
summer (from Cfb to Csa) southwest, and to a temperate climate without
a dry season (from Cfb to Csb) northwest, after 2040 with RCPs 4.5 and
8.5 (Figure~\ref{fig6} and
Supplementary Figure~S3). Under the high-emission scenario, some
areas in Brittany (northwest France) might also change a second time
from warm to hot summers (from Csb to Csa) by the end of the century
(Figure~\ref{fig6} and
Supplementary Figure~S3). 

The mountainous regions would transition from a cold climate without a
dry season and a warm summer to a temperate climate (from Dfb to Cfb).
This trend starts by 2000 in the low-altitude regions and spreads in
higher mountains until 2040 regardless of the emission scenario 
(Figure~\ref{fig6}), although the higher altitudes would see climate
type changes with RCPs 4.5 and 8.5 only. 

Furthermore, climate types in mountainous regions are layered based on
elevation: 0--650~m for Cfa, Cfb, and Csa, and 200--1500~m,
450--1900~m,  1100--2800~m, and 1600--3300~m for Csb, Dfb, Dfc, and ET,
respectively, over the historical period  (Figure~\ref{fig7}). For the
high-emission scenario, an upward shift of approximately 1000 and 650~m
is expected for the Dfb and Dfc climate types, respectively, while the
ET climate type disappears by the end of the century (corresponding to
a mean vertical velocity of approximately 11, 6.4, and
$6.3~\mathrm{m}{\cdot}\mathrm{yr}^{-1}$, respectively). These
mountainous Dfb, Dfc, and ET climate types will be replaced by the Cfb
climate type under the high-emission scenario  (Figures~\ref{fig4}
and~\ref{fig7}). Reversely, a downward shift of approximately 500~m is
expected for the Csb climate type, which was mostly located on Corsica
in 1976--2005, while we expect it to be located in Britain region in 
2070--2099 (Figures~\ref{fig2} and~\ref{fig7}). 

\begin{figure*}
\includegraphics{fig07}
\caption{\label{fig7}Distributions of the elevations for each climate
type (the most frequent one among climate projections) for RCPs 2.6,
4.5, and 8.5 and for each period. Statistics are displayed by boxplots
where the lower and upper hinges correspond to the 25th and 75th
quantiles, the horizontal bar is the median, the upper and lower
whiskers extend up to 1.5 times the interquartile distance from the
25th and 75th quantiles, and the dots are the outliers beyond the end
of the whiskers.}
\vspace*{-6pt}
\end{figure*}

\section{Discussion}\label{sec4}

\subsection{Climate changes both horizontally and\newline
vertically}\label{sec4.1}
Climate types in France for the historical 1976--2005 period were
mostly a temperate climate (Cfb, Csa, Csb, and Cfa) located in the
low-elevations areas, followed by a cold climate (Dfb and Dfc) located
in the mid-mountainous region, and a polar climate (ET) located in the
high-elevation areas. 

These results are well correlated with those of other studies on the
mean climate types in France during past periods. Indeed, 
\citet{Dubreuil2022} and  \citet{Evenoetal2016} also applied the
K\"{o}ppen--Geiger classification and found that Cfb was the main
climate type observed in France, and that the Csa climate type was
restricted to the Mediterranean  region.  \citet{Jolyetal2010} computed
climate types using factor analysis and found similar patterns with a
large influence of oceanic climate from the west to the east of France,
and Mediterranean climate in south-east, which they explained by the
barrier effect of the mountains limiting the spread of these climate
types.

Climate types computed with the climate projections are consistent with
those computed with observed data across France in the historical
1976--2005 period. This shows the ability of climate models to simulate
future air temperature and precipitation, and thus future climate types
based on climate\unskip\break 
projections. Nevertheless, this good agreement in the
historical period was expected since the bias correction methods were
calibrated on the same temperature and precipitation data and on
similar periods. Even though the climate types computed with the model
outputs showed good agreement rates for the historical period, the
agreement rates slightly decrease in the future, meaning that different
futures are possible. However, the long-term dynamics (i.e., increasing
or decreasing trends in the proportion of area covered by a climate
type) remain consistent within climate projections. Thus, climate
models agree on the change in climate type, but can diverge in terms of
temporality.

In the near future, changes in climate types are similar for the three
RCPs, but major changes are expected in the far future for the
high-emission scenario. This emission scenario would lead to faster
climate type changes and over a larger area in the future. Climate type
transitions are expected spatially both horizontally and vertically
(i.e., in mountainous regions). A major transition is expected from the
temperate climate without a dry season and warm summer to the temperate
climate without a dry season and hot summer. These results are in line
with the  ``Mediterraneanisation'' of the climate in France  mentioned
by  \citet{Dubreuil2022}, who also observed a lower interannual
variability of annual climate types under RCP 8.5 by the end of the
century. Indeed, \citet{Evenoetal2016} and  \citet{Dubreuil2022}
showed that computing the classification on annual scales (vs. a
multi-year mean as done in the present study) could result in different
climate types attribution, highlighting the potential interannual
variability of\unskip\break 
climate.

In addition, the complexity of very local climates may not be well
reflected by global-scale methods such as the K\"{o}ppen--Geiger
classification  \citep{Jolyetal2010}. Therefore, some modifications of
its criteria may be  recommended 
\citep{Evenoetal2016,PlanchonRosier2005}. For example, a modification
of the criterion used to define the dry season in temperature region
(Cs) was suggested by  \citet{BagnoulsGaussen1957}. We applied this
modification (i.e., the climate is considered as belonging to Cs when
the monthly precipitation is lower than 2 times monthly temperature in
June, July, or August) to our dataset. We found that in 2070--2099 under
RCP 8.5, the climate type Csa covered a larger part of France, while
climate types during the historical period were very similar, except
for north western France where the modified classification leads to a
drier climate (Csb), which seems less plausible\unskip\break  
(Supplementary Figure~S4). 

The transition to drier and warmer climate type is expected to rapidly
spread northward, at an unprecedented speed of approximately
$7~\mathrm{km}{\cdot}\mathrm{yr}^{-1}$ horizontally and 
$5~\mathrm{m}{\cdot}\mathrm{yr}^{-1}$ vertically, although these speeds
were not uniformly observed across the country. Similar northward
migrations of agro-climatic zones were observed across Europe over the
past few  decades  \citep{Ceglaretal2019}. 

As mentioned by  \citet{Dubreuil2022}, the K\"{o}ppen--Geiger climates
type are computed on temperature and precipitation data only, thus
ignoring how other climate variables may change (e.g. wind speed, cloud
cover, air humidity, etc.). Therefore, similar climate types for
different locations at different time periods (also known as climate
analogues) may not cover exactly the same climate conditions (i.e.\ Csa
climate type in the north may be slightly different from Csa in the
south of France). 

\subsection{Impact of new climate conditions}\label{sec4.2}
The velocities of horizontal and vertical changes raise serious
questions about the ability of our societies and ecosystems to adapt to
new climate conditions in such short time scales under a high-emission
scenario. Vegetation in natural forests has two strategies to adapt to
changing climatic conditions: resilience or  migration 
\citep{CorlettWestcott2013,LancasterHumphreys2020,Thompsonetal2009}.
The vertical migration of mountain tree lines has been observed at a
mean velocity of  $1.2~\mathrm{m}{\cdot}\mathrm{yr}^{-1}$ (ranging from
${-}$10.2 to  $16.9~\mathrm{m}{\cdot}\mathrm{yr}^{-1}$ in boreal,
temperate, and tropical regions) in relation to climate changes 
\citep{Heetal2023}. 

The horizontal migration velocities of natural forest range between 200
and  $1500~\mathrm{m}{\cdot}\mathrm{yr}^{-1}$
\citep{CorlettWestcott2013,Iversonetal2004}, which is much slower than
the $7~\mathrm{km}{\cdot}\mathrm{yr}^{-1}$ that we obtained. These
rapid changes will very likely cause massive forest dieback for many
species, or favor invasive species. In addition, this climate change
may also limit the potential of vegetation to accumulate carbon 
\citep{Higginsetal2023}. Rapid changes in vegetation cover could affect
the water cycle through evaporative and energy fluxes that depend on
land use and vegetation type  \citep{VajdaVenalainen2005}.  It may for
example lead to stronger extreme floods in the Alps 
\citep{Wilhelmetal2022}. The cascading impacts on water resources and
on water-dependent energy sources have also been focused on strongly at
both the global and national scales 
\citep{Ahmad2021,Chauveauetal2013,LemaitreBassetetal2021,Linnerudetal2011,RTE2021}, 
which reflects the increasing concerns regarding adaptation needs
across a wide range of socioeconomic sectors.

Our results suggest that changes in climate types can be more limited
under the low-emission scenario, both in terms of spatial and temporal
dynamics. With this emission scenario, changes in climate types would
be constrained to smaller areas located mainly in mountainous regions
and in southwest France, and the velocities of change should be more
consistent with a natural and achievable adaptation speed of ecosystems
and society. However, this low-emission scenario is far from warranted,
and the worst-case scenarios need to be considered with attention.
Finally, we would like to stress that many European countries share the
diverse climatic and topographical conditions found in France, and that
similar changes are therefore expected throughout \mbox{Europe.}

\section{Conclusion}\label{sec5}
Climate maps are often taught in high schools to introduce the notion
of climate and therefore represent good candidates to raise public
awareness on the
impacts of climate change. We applied the
K\"{o}ppen--Geiger classification across France on a high-resolution
grid with bias-corrected climate projections to assess past and future
changes in climate conditions.

Our results show good agreement between climate types computed with
climate projections and reanalysis climate data for the 1976--2005
period. Changes in climate types are expected in the near future for
approximately 20\% of France, regardless of the emission scenario.
However, major changes are expected after 2040 under RCP 8.5 for 86\%
of France, towards warmer and drier summers, leading to a massive
expansion of the temperate, hot summer with and without dry season (Csa
and Cfa, respectively), as well as the expansion of temperate climate
in mountainous regions. These changes could reach an average transition
speed of up to 
$7~\mathrm{km}{\cdot}\mathrm{yr}^{-1}$ northwards, with
unprecedented consequences on agriculture, biodiversity, and water and
energy management, not to mention on societies.

This work aims at contributing to the general audience's understanding
of the consequences of climate change we are experiencing as a global
society. We believe that it may help inform decision-makers in
designing mitigation and adaptation plans in France, as well as inspire
similar studies worldwide. It advocates for rapid and significant
mitigation measures and illustrates the kind of future environments we
would need to adapt to in our lifetime. 

\section*{Declaration of interests}
The authors do not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and have
declared no affiliations other than their research organizations.

\section*{Funding}
This study was carried out within the Explore2 project funded by the
French Ministry for Ecology (contract no.~2103295967). The postdoctoral
position of the first author was funded by the same project. 

\section*{Acknowledgements}
The SAFRAN climate reanalysis was provided by M\'{e}t\'{e}o-France and
the climate projections were \mbox{retrieved} from the DRIAS website and made
available by M\'{e}t\'{e}o-France and the Institut Pierre-Simon Laplace
(IPSL). The authors thank the Euro-CORDEX initiative for making
available the raw regionalized climate projections, as well as Olivier
Planchon for his precious help during the revision of the manuscript. 

\CDRGrant[French Ministry for Ecology]{2103295967} 

\back{}

\section*{Supplementary data}
Supporting information for this article is available on the journal's
website under \printDOI\ or from the author.  

\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{crgeos-263-suppl.pdf}}

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