# Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, Antoinette Alias

► To cite this version:

Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, et al. Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?. Natural Hazards and Earth System Sciences, 2024, 24 (4), pp.1163-1183. 10.5194/nhess-24-1163-2024 . hal-04538579

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# Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, Antoinette Alias

<figure><img src="image_1.png" /></figure>

## To cite this version:

Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, et al. Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?. Natural Hazards and Earth System Sciences, 2024, 24 (4), pp.1163-1183. 10.5194/nhess-24-1163-2024. hal-04538579

HAL Id: hal-04538579

https://hal.science/hal-04538579v1

Submitted on 9 Apr 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
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# Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Nils Poncet<fnref n="1" />, Philippe Lucas-Picher<fnref n="1" />,<fnref n="2" />, Yves Tramblay<fnref n="3" />, Guillaume Thirel<fnref n="4" />,<fnref n="1" />, Humberto Vergara<fnref n="5" />,<fnref n="6" />, Jonathan Gourley<fnref n="5" />, and Antoinette Alias<fnref n="1" />

**Correspondence:** Nils Poncet (nilsponcet@hotmail.fr)

Received: 24 May 2023 – Discussion started: 5 June 2023

Revised: 29 December 2023 – Accepted: 14 January 2024 – Published: 3 April 2024

**Abstract.** Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM,

the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.

## 1 Introduction

Every year, the French Mediterranean region faces intense flash floods during the fall season, causing important damages and casualties. Even if only small to medium catchments are concerned, flash floods are amongst the most destructive hazards in the French Mediterranean region (Boudou et al., 2016; Vinet et al., 2022). These hydrological events are triggered by heavy precipitation events (HPEs) that can bring up to half of the annual rainfall in a few hours to days (Delrieu et al., 2005; Nuissier et al., 2011; Ricard et al., 2012). Initiated by the complex interaction between moisture fluxes from the Mediterranean Sea to the atmosphere, synoptic-scale processes and topography, HPEs are

<footnote n="1">¹Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France</footnote>

<footnote n="2">²Département Des Sciences de La Terre Et de L'atmosphère, Université du Québec À Montréal, Montréal, QC, Canada</footnote>

<footnote n="3">³Espace-Dev, Univ. Montpellier, IRD, Montpellier, France</footnote>

<footnote n="4">⁴Université Paris-Saclay, INRAE, HYCAR Research Unit, Antony, France</footnote>

<footnote n="5">⁵Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USA</footnote>

<footnote n="6">⁶NOAA National Severe Storms Laboratory, Norman, Oklahoma, USA</footnote>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

# Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Nils Poncet<fnref n="1" />, Philippe Lucas-Picher<fnref n="1" />,<fnref n="2" />, Yves Tramblay<fnref n="3" />, Guillaume Thirel<fnref n="4" />,<fnref n="1" />, Humberto Vergara<fnref n="5" />,<fnref n="6" />, Jonathan Gourley<fnref n="5" />, and Antoinette Alias<fnref n="1" />

**Correspondence:** Nils Poncet (nilsponcet@hotmail.fr)

Received: 24 May 2023 – Discussion started: 5 June 2023

Revised: 29 December 2023 – Accepted: 14 January 2024 – Published: 3 April 2024

**Abstract.** Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM,

the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.

## 1 Introduction

Every year, the French Mediterranean region faces intense flash floods during the fall season, causing important damages and casualties. Even if only small to medium catchments are concerned, flash floods are amongst the most destructive hazards in the French Mediterranean region (Boudou et al., 2016; Vinet et al., 2022). These hydrological events are triggered by heavy precipitation events (HPEs) that can bring up to half of the annual rainfall in a few hours to days (Delrieu et al., 2005; Nuissier et al., 2011; Ricard et al., 2012). Initiated by the complex interaction between moisture fluxes from the Mediterranean Sea to the atmosphere, synoptic-scale processes and topography, HPEs are

<footnote n="1">¹Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France</footnote>

<footnote n="2">²Département Des Sciences de La Terre Et de L'atmosphère, Université du Québec À Montréal, Montréal, QC, Canada</footnote>

<footnote n="3">³Espace-Dev, Univ. Montpellier, IRD, Montpellier, France</footnote>

<footnote n="4">⁴Université Paris-Saclay, INRAE, HYCAR Research Unit, Antony, France</footnote>

<footnote n="5">⁵Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USA</footnote>

<footnote n="6">⁶NOAA National Severe Storms Laboratory, Norman, Oklahoma, USA</footnote>
complex and challenging to simulate and forecast with pre-
cision (Khodayar et al., 2018; Caillaud et al., 2021; Caumont
et al., 2021). Due to their strong societal and economic im-
pacts, being able to model HPEs and their resulting flash
floods in current and future climate is an important societal
concern.

For several decades, faster computational capabilities and
improved understanding of atmospheric processes have en-
hanced the confidence towards climate model simulations
at global and regional scales (Rummukainen, 2010; Giorgi,
2019). Using a limited area, regional climate models (RCMs)
can reach spatial resolutions down to 10 km by dynami-
cally downscaling global climate model (GCM) simulations
(Giorgi and Gutowski, 2015). The increase in climate model
spatial resolutions with time brought a more accurate de-
scription of the topography and an improved simulation of
physical processes, improving the simulation of regional
meteorological phenomena such as extreme rainfall events
(Giorgi, 2019). Due to their higher spatial resolutions, RCMs
allow the study of climate change impacts at the regional
scale (Maurer et al., 2007; Teutschbein and Seibert, 2010).
Namely, numerous studies have used RCM simulations as in-
puts for hydrological models to simulate discharge and floods
in Europe (Kay et al., 2006; Dankers and Feyen, 2009; Kö-
plin et al., 2014). Often necessary for hydrological simula-
tions, bias correction methods can substantially affect the
projection of floods in a warmer climate (Boé et al., 2007;
Rojas et al., 2011; Teutschbein and Seibert, 2012).

RCM projections generally agree on the increase in ex-
treme precipitation in the French Mediterranean region
(Tramblay and Somot, 2018; Zittis et al., 2021), confirming
the observed trends (Ribes et al., 2019). Despite the positive
trends in rainfall extremes over the French Mediterranean
region, the link of this signal to floods is not straightfor-
ward (Sharma et al., 2018; Tramblay et al., 2019). Hydro-
logical trends depend on multiple factors, such as catchment
location, event severity, flood-generating processes and soil
moisture conditions (Blöschl et al., 2019; Wasko et al., 2023;
Brunner et al., 2021). In the Mediterranean area, the reduc-
tion of the soil moisture prior to flood events could coun-
terbalance rainfall extremes and possibly invert the sign of
observed flood changes (Tramblay et al., 2023). In terms of
future trends, the signal on flood magnitude and frequency
thus remains uncertain in the French Mediterranean region.
Using daily variables from an RCM ensemble, Alfieri et al.
(2015) showed a future decrease in mean annual flows and an
increase in flood frequency in this area. Thober et al. (2018)
showed no clear signal for high flows and flood magnitudes
in the French Mediterranean. On the contrary, Lemaitre-
Basset et al. (2021) reported a projected increase in flood
severity in a catchment in southern France.

Under a Mediterranean climate, precipitation is usually
the main driver for runoff production. Floods are therefore
mainly triggered by HPEs on small catchments (Ampon-
sah et al., 2018). Despite RCMs' good simulation of cli-

matic conditions, biases remain in the representation of some
regional and local phenomena, such as HPEs (Khodayar
et al., 2016; Caillaud et al., 2021). Indeed, with resolutions
coarser than 10 km, the simulation of convective events with
RCMs requires the use of deep convection parameterization
schemes, leading to an underestimation of rainfall extremes
(Prein et al., 2016; Ban et al., 2021). This poor representa-
tion of sub-daily extreme rainfall by RCMs could question
the reliability of the flood impact studies over small Mediter-
ranean catchments, perhaps explaining some contradictory
results identified in the literature.

During the last decade, a new generation of RCMs has
emerged (Kendon et al., 2012). Convection-permitting re-
gional climate models (CPMs) generally have a resolution
finer than 5 km. Their resolution is sufficiently fine to al-
low an explicit representation of deep convective processes
(Lucas-Picher et al., 2021) and thus removes the need for
deep convection parameterization schemes, which are nec-
essary in RCMs. Most of the studies using CPMs show a
clear added value compared to RCMs in the representation
of local-scale phenomena such as convective cells and local-
ized intense precipitation (Prein et al., 2015; Coppola et al.,
2020; Caillaud et al., 2021; Caldas-Alvarez et al., 2023). In
the context of the Coordinated Regional Climate Downscal-
ing Experiment Flagship Pilot Study (CORDEX-FPS) con-
vection initiative, Ban et al. (2021) carried out a multimodel
evaluation of CPMs over a central European domain. This
study confirmed the added value of different CPMs in the
simulation of hourly extreme precipitation.

Even if CPMs are a promising tool to study hydrologi-
cal impacts, only a few of them have yet been used for this
purpose. Results from these preliminary studies do not indi-
cate improvements in discharge simulation and flood mod-
eling using CPMs. Kay et al. (2015) used a CPM output to
feed a hydrological model over river basins in Great Britain.
Their results indicate no added value using this CPM on dis-
charge modeling, with strong geographical differences. The
same conclusion was found by Reszler et al. (2018) using
simulations from two CPMs as input for the KAMPUS dis-
tributed hydrological model over a continental and mountain-
ous domain (Eastern Alps). Mendoza et al. (2016) compared
the impact of climate model spatial resolution in Colorado,
showing the ability of CPMs to reproduce observed annual
cycles especially in mountainous catchments. In an idealized
modeling chain with different climate simulation resolution,
Quintero et al. (2021) found that a 4 km grid spacing CPM is
the best compromise between computational costs and per-
formance of hydrological modeling. In terms of future pro-
jections, and using CPMs as an input of a distributed hydro-
logical model, floods are projected to become more severe,
more frequent, more unpredictable and flashier in the USA
(Li et al., 2022a, b). In a recent study, Kay (2022) used an
ensemble of CPMs to feed a gridded hydrological model,
showing a better performance and higher future flow changes
in CPMs compared to RCMs. Using a modeling chain driven
complex and challenging to simulate and forecast with pre-
cision (Khodayar et al., 2018; Caillaud et al., 2021; Caumont
et al., 2021). Due to their strong societal and economic im-
pacts, being able to model HPEs and their resulting flash
floods in current and future climate is an important societal
concern.

For several decades, faster computational capabilities and
improved understanding of atmospheric processes have en-
hanced the confidence towards climate model simulations
at global and regional scales (Rummukainen, 2010; Giorgi,
2019). Using a limited area, regional climate models (RCMs)
can reach spatial resolutions down to 10 km by dynami-
cally downscaling global climate model (GCM) simulations
(Giorgi and Gutowski, 2015). The increase in climate model
spatial resolutions with time brought a more accurate de-
scription of the topography and an improved simulation of
physical processes, improving the simulation of regional
meteorological phenomena such as extreme rainfall events
(Giorgi, 2019). Due to their higher spatial resolutions, RCMs
allow the study of climate change impacts at the regional
scale (Maurer et al., 2007; Teutschbein and Seibert, 2010).
Namely, numerous studies have used RCM simulations as in-
puts for hydrological models to simulate discharge and floods
in Europe (Kay et al., 2006; Dankers and Feyen, 2009; Kö-
plin et al., 2014). Often necessary for hydrological simula-
tions, bias correction methods can substantially affect the
projection of floods in a warmer climate (Boé et al., 2007;
Rojas et al., 2011; Teutschbein and Seibert, 2012).

RCM projections generally agree on the increase in ex-
treme precipitation in the French Mediterranean region
(Tramblay and Somot, 2018; Zittis et al., 2021), confirming
the observed trends (Ribes et al., 2019). Despite the positive
trends in rainfall extremes over the French Mediterranean
region, the link of this signal to floods is not straightfor-
ward (Sharma et al., 2018; Tramblay et al., 2019). Hydro-
logical trends depend on multiple factors, such as catchment
location, event severity, flood-generating processes and soil
moisture conditions (Blöschl et al., 2019; Wasko et al., 2023;
Brunner et al., 2021). In the Mediterranean area, the reduc-
tion of the soil moisture prior to flood events could coun-
terbalance rainfall extremes and possibly invert the sign of
observed flood changes (Tramblay et al., 2023). In terms of
future trends, the signal on flood magnitude and frequency
thus remains uncertain in the French Mediterranean region.
Using daily variables from an RCM ensemble, Alfieri et al.
(2015) showed a future decrease in mean annual flows and an
increase in flood frequency in this area. Thober et al. (2018)
showed no clear signal for high flows and flood magnitudes
in the French Mediterranean. On the contrary, Lemaitre-
Basset et al. (2021) reported a projected increase in flood
severity in a catchment in southern France.

Under a Mediterranean climate, precipitation is usually
the main driver for runoff production. Floods are therefore
mainly triggered by HPEs on small catchments (Ampon-
sah et al., 2018). Despite RCMs' good simulation of cli-

matic conditions, biases remain in the representation of some
regional and local phenomena, such as HPEs (Khodayar
et al., 2016; Caillaud et al., 2021). Indeed, with resolutions
coarser than 10 km, the simulation of convective events with
RCMs requires the use of deep convection parameterization
schemes, leading to an underestimation of rainfall extremes
(Prein et al., 2016; Ban et al., 2021). This poor representa-
tion of sub-daily extreme rainfall by RCMs could question
the reliability of the flood impact studies over small Mediter-
ranean catchments, perhaps explaining some contradictory
results identified in the literature.

During the last decade, a new generation of RCMs has
emerged (Kendon et al., 2012). Convection-permitting re-
gional climate models (CPMs) generally have a resolution
finer than 5 km. Their resolution is sufficiently fine to al-
low an explicit representation of deep convective processes
(Lucas-Picher et al., 2021) and thus removes the need for
deep convection parameterization schemes, which are nec-
essary in RCMs. Most of the studies using CPMs show a
clear added value compared to RCMs in the representation
of local-scale phenomena such as convective cells and local-
ized intense precipitation (Prein et al., 2015; Coppola et al.,
2020; Caillaud et al., 2021; Caldas-Alvarez et al., 2023). In
the context of the Coordinated Regional Climate Downscal-
ing Experiment Flagship Pilot Study (CORDEX-FPS) con-
vection initiative, Ban et al. (2021) carried out a multimodel
evaluation of CPMs over a central European domain. This
study confirmed the added value of different CPMs in the
simulation of hourly extreme precipitation.

Even if CPMs are a promising tool to study hydrologi-
cal impacts, only a few of them have yet been used for this
purpose. Results from these preliminary studies do not indi-
cate improvements in discharge simulation and flood mod-
eling using CPMs. Kay et al. (2015) used a CPM output to
feed a hydrological model over river basins in Great Britain.
Their results indicate no added value using this CPM on dis-
charge modeling, with strong geographical differences. The
same conclusion was found by Reszler et al. (2018) using
simulations from two CPMs as input for the KAMPUS dis-
tributed hydrological model over a continental and mountain-
ous domain (Eastern Alps). Mendoza et al. (2016) compared
the impact of climate model spatial resolution in Colorado,
showing the ability of CPMs to reproduce observed annual
cycles especially in mountainous catchments. In an idealized
modeling chain with different climate simulation resolution,
Quintero et al. (2021) found that a 4 km grid spacing CPM is
the best compromise between computational costs and per-
formance of hydrological modeling. In terms of future pro-
jections, and using CPMs as an input of a distributed hydro-
logical model, floods are projected to become more severe,
more frequent, more unpredictable and flashier in the USA
(Li et al., 2022a, b). In a recent study, Kay (2022) used an
ensemble of CPMs to feed a gridded hydrological model,
showing a better performance and higher future flow changes
in CPMs compared to RCMs. Using a modeling chain driven
by a CPM over a tropical area in Africa, Ascott et al. (2023) indicate no significant trend in floods in future projections. Even though the aforementioned studies have pioneered the use of CPMs with hydrological models, they are limited to only one hydrological model, ignoring uncertainty induced by hydrological models discrepancies. To our knowledge, no paper has studied Mediterranean floods using a CPM.

In this study, we would like to assess the added value of a CPM regarding the evolution of floods over a Mediterranean catchment prone to intense floods. For this, we perform an analysis of simulated flood magnitudes and characteristics under a historical scenario and under the RCP8.5 emissions scenario. Two climate datasets, a CPM (CNRM-AROME) and its driving RCM (CNRM-ALADIN), are used as inputs to one lumped, conceptual hydrological model (GR5H) and one distributed, process-based hydrological model (CREST used in EF5). The main aims of this paper are to

- evaluate the added value of the CPM on the simulation of extreme rainfall at the scale of a small Mediterranean basin;

- evaluate the capacity of a CPM to reproduce Mediterranean floods using two hydrological models;

- assess future changes in flood distributions and characteristics between the two models and two climate simulations.

Section 2 describes the area of interest, the data, and the different climate and hydrologic models. The methodology is detailed in Sect. 3. The evaluation of the climate and hydrological models and the projection of floods are presented and discussed in Sect. 4.

# 2 Study area and data

## 2.1 Catchment: Gardon d'Anduze

The study is performed over a 543 km² Mediterranean catchment, the Gardon d'Anduze, located in the Cévennes region, on the southern slopes of the Massif Central mountain range. The Gardon d'Anduze has a complex topography ranging from 130 m to 1200 m a.s.l. Consequently, it is rather natural, forested and lightly urbanized. Indeed, most of its surface is covered by typical Mediterranean vegetation. The soils are relatively thin, from 10 cm at the top of the hillslopes to 100 cm close to the river bed (Vannier et al., 2014). It is considered a highly reactive Mediterranean catchment, known for experiencing some of the most destructive flash floods in France (Delrieu et al., 2005; Toukourou et al., 2011). The Gardon d'Anduze catchment has been extensively studied due to its location of particular interest in hydrology, especially for flash flood modeling and forecasting (Alfieri et al., 2011; Roux et al., 2011; Moussa, 2010). Figure 1 displays the location of Gardon d'Anduze in France (a) and orography as

shown by an 80 m resolution DEM (b) and by the RCM and CPM (c and d).

## 2.2 Climate models

In this study, we use climate simulations from a CPM and its forcing RCM, both developed and released at the Centre National de Recherches Météorologiques (CNRM), in Toulouse, France. Information about these two climate models is summarized in Table 1.

CNRM-ALADIN is a 12 km grid cell RCM that has been run over the continental EUR-11 domain through the EUROCORDEX initiative. Retrospective simulations are driven by the ERA-Interim global reanalysis dataset (Dee et al., 2011), while historical and future scenarios are forced by the CNRM-CM5 global model (Voldoire et al., 2013). This version is derived from the development of the NWP model ALADIN, thanks to the ACCORD research centers consortium. CNRM-ALADIN has been extensively used in the CORDEX framework over Europe, the Mediterranean, North America and Africa. For more details about the parameterization schemes and configurations of the last version of ALADIN, see Nabat et al. (2020) and Lucas-Picher et al. (2023).

CNRM-AROME is a CPM, which is adapted from cycle 41 of the NWP AROME formerly operating for numerical weather prediction. Simulations used in this study are produced, as part of the EUCP project, over the NW domain, covering France, the UK, the north of Spain and most of Germany with a 2.5 km mesh. Some papers have already evaluated this model, under a former release (Fumière et al., 2020), a different domain and forcing RCM version (Caillaud et al., 2021; Monteiro et al., 2022), or the same version and domain used in this study (Lucas-Picher et al., 2023). All these evaluations have established the added value of CNRM-AROME in the reproduction of extreme rainfall and HPEs compared to CNRM-ALADIN over the domains. To our knowledge, no published study has used CNRM-AROME projections for climate change assessment. In this paper, ALADIN and AROME refer to the version of CNRM-ALADIN and CNRM-AROME described above, respectively.

## 2.3 Observations

In this study, we compare the hourly simulated data with a high-resolution observed precipitation dataset called COMEPHORE. COMEPHORE is an hourly 1 km resolution gridded product covering Metropolitan France during the 1997–2019 period. COMEPHORE was built by merging weather station rainfall measurements and different radar sources (Laurantin et al., 2012; Caillaud et al., 2021). The dataset used approximately 3000 daily rain gauges and 1200 hourly rain gauges. The number of radars has more than doubled during the first decade of data. In 2019, COMEPHORE was built using data from 29 radars comprising the French radar network (ARAMIS), in addition to
by a CPM over a tropical area in Africa, Ascott et al. (2023) indicate no significant trend in floods in future projections. Even though the aforementioned studies have pioneered the use of CPMs with hydrological models, they are limited to only one hydrological model, ignoring uncertainty induced by hydrological models discrepancies. To our knowledge, no paper has studied Mediterranean floods using a CPM.

In this study, we would like to assess the added value of a CPM regarding the evolution of floods over a Mediterranean catchment prone to intense floods. For this, we perform an analysis of simulated flood magnitudes and characteristics under a historical scenario and under the RCP8.5 emissions scenario. Two climate datasets, a CPM (CNRM-AROME) and its driving RCM (CNRM-ALADIN), are used as inputs to one lumped, conceptual hydrological model (GR5H) and one distributed, process-based hydrological model (CREST used in EF5). The main aims of this paper are to

- evaluate the added value of the CPM on the simulation of extreme rainfall at the scale of a small Mediterranean basin;

- evaluate the capacity of a CPM to reproduce Mediterranean floods using two hydrological models;

- assess future changes in flood distributions and characteristics between the two models and two climate simulations.

Section 2 describes the area of interest, the data, and the different climate and hydrologic models. The methodology is detailed in Sect. 3. The evaluation of the climate and hydrological models and the projection of floods are presented and discussed in Sect. 4.

# 2 Study area and data

## 2.1 Catchment: Gardon d'Anduze

The study is performed over a 543 km² Mediterranean catchment, the Gardon d'Anduze, located in the Cévennes region, on the southern slopes of the Massif Central mountain range. The Gardon d'Anduze has a complex topography ranging from 130 m to 1200 m a.s.l. Consequently, it is rather natural, forested and lightly urbanized. Indeed, most of its surface is covered by typical Mediterranean vegetation. The soils are relatively thin, from 10 cm at the top of the hillslopes to 100 cm close to the river bed (Vannier et al., 2014). It is considered a highly reactive Mediterranean catchment, known for experiencing some of the most destructive flash floods in France (Delrieu et al., 2005; Toukourou et al., 2011). The Gardon d'Anduze catchment has been extensively studied due to its location of particular interest in hydrology, especially for flash flood modeling and forecasting (Alfieri et al., 2011; Roux et al., 2011; Moussa, 2010). Figure 1 displays the location of Gardon d'Anduze in France (a) and orography as

shown by an 80 m resolution DEM (b) and by the RCM and CPM (c and d).

## 2.2 Climate models

In this study, we use climate simulations from a CPM and its forcing RCM, both developed and released at the Centre National de Recherches Météorologiques (CNRM), in Toulouse, France. Information about these two climate models is summarized in Table 1.

CNRM-ALADIN is a 12 km grid cell RCM that has been run over the continental EUR-11 domain through the EUROCORDEX initiative. Retrospective simulations are driven by the ERA-Interim global reanalysis dataset (Dee et al., 2011), while historical and future scenarios are forced by the CNRM-CM5 global model (Voldoire et al., 2013). This version is derived from the development of the NWP model ALADIN, thanks to the ACCORD research centers consortium. CNRM-ALADIN has been extensively used in the CORDEX framework over Europe, the Mediterranean, North America and Africa. For more details about the parameterization schemes and configurations of the last version of ALADIN, see Nabat et al. (2020) and Lucas-Picher et al. (2023).

CNRM-AROME is a CPM, which is adapted from cycle 41 of the NWP AROME formerly operating for numerical weather prediction. Simulations used in this study are produced, as part of the EUCP project, over the NW domain, covering France, the UK, the north of Spain and most of Germany with a 2.5 km mesh. Some papers have already evaluated this model, under a former release (Fumière et al., 2020), a different domain and forcing RCM version (Caillaud et al., 2021; Monteiro et al., 2022), or the same version and domain used in this study (Lucas-Picher et al., 2023). All these evaluations have established the added value of CNRM-AROME in the reproduction of extreme rainfall and HPEs compared to CNRM-ALADIN over the domains. To our knowledge, no published study has used CNRM-AROME projections for climate change assessment. In this paper, ALADIN and AROME refer to the version of CNRM-ALADIN and CNRM-AROME described above, respectively.

## 2.3 Observations

In this study, we compare the hourly simulated data with a high-resolution observed precipitation dataset called COMEPHORE. COMEPHORE is an hourly 1 km resolution gridded product covering Metropolitan France during the 1997–2019 period. COMEPHORE was built by merging weather station rainfall measurements and different radar sources (Laurantin et al., 2012; Caillaud et al., 2021). The dataset used approximately 3000 daily rain gauges and 1200 hourly rain gauges. The number of radars has more than doubled during the first decade of data. In 2019, COMEPHORE was built using data from 29 radars comprising the French radar network (ARAMIS), in addition to
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Location of the Gardon d'Anduze catchment (in red) over France (a) and orography of this catchment represented by an 80 m DEM (b), by the 12 km CNRM-ALADIN RCM (c) and by the 2.5 km CNRM-AROME CPM (d).</figcaption></figure>

<table><caption>**Table 1.** Information about the two climate models and their associated simulations used in this study.</caption><thead><tr><th>Model name</th><th>CNRM-ALADIN</th><th>CNRM-AROME</th></tr></thead><tbody><tr><td>Version</td><td>6.3</td><td>41t1</td></tr><tr><td>Resolution</td><td>12 km</td><td>2.5 km</td></tr><tr><td>Retrospective simulation (evaluation)</td><td>1979–2018</td><td>2000–2018</td></tr><tr><td>Historical simulation</td><td>1951–2005</td><td>1986–2005</td></tr><tr><td>Future simulation (RCP8.5)</td><td>2006–2100</td><td>2080–2099</td></tr><tr><td>Deep convection</td><td>parameterized</td><td>explicit</td></tr><tr><td>Reference papers</td><td>Nabat et al. (2020)</td><td>Lucas-Picher et al. (2023)</td></tr></tbody></table>

radars from the Swiss network and another one on Jersey Island. Even though the quality of the dataset is not temporally and spatially homogeneous and it has a tendency of underestimating some extreme values (Laurantin et al., 2012), COMEPHORE can be considered the best national precipitation product for studying hourly rainfall at high spatial and temporal resolutions. Furthermore, the Gardon d'Anduze

river catchment is located in a region of high rain gauges and radar density, raising the confidence in this dataset for a benchmark in this study (Caillaud et al., 2021).

Temperature is the main variable to compute potential evapotranspiration (PET). As a reference, we extracted 3 h temperature measurements from six weather stations in the basin. Temperature was then linearly downscaled to the
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Location of the Gardon d'Anduze catchment (in red) over France (a) and orography of this catchment represented by an 80 m DEM (b), by the 12 km CNRM-ALADIN RCM (c) and by the 2.5 km CNRM-AROME CPM (d).</figcaption></figure>

<table><caption>**Table 1.** Information about the two climate models and their associated simulations used in this study.</caption><thead><tr><th>Model name</th><th>CNRM-ALADIN</th><th>CNRM-AROME</th></tr></thead><tbody><tr><td>Version</td><td>6.3</td><td>41t1</td></tr><tr><td>Resolution</td><td>12 km</td><td>2.5 km</td></tr><tr><td>Retrospective simulation (evaluation)</td><td>1979–2018</td><td>2000–2018</td></tr><tr><td>Historical simulation</td><td>1951–2005</td><td>1986–2005</td></tr><tr><td>Future simulation (RCP8.5)</td><td>2006–2100</td><td>2080–2099</td></tr><tr><td>Deep convection</td><td>parameterized</td><td>explicit</td></tr><tr><td>Reference papers</td><td>Nabat et al. (2020)</td><td>Lucas-Picher et al. (2023)</td></tr></tbody></table>

radars from the Swiss network and another one on Jersey Island. Even though the quality of the dataset is not temporally and spatially homogeneous and it has a tendency of underestimating some extreme values (Laurantin et al., 2012), COMEPHORE can be considered the best national precipitation product for studying hourly rainfall at high spatial and temporal resolutions. Furthermore, the Gardon d'Anduze

river catchment is located in a region of high rain gauges and radar density, raising the confidence in this dataset for a benchmark in this study (Caillaud et al., 2021).

Temperature is the main variable to compute potential evapotranspiration (PET). As a reference, we extracted 3 h temperature measurements from six weather stations in the basin. Temperature was then linearly downscaled to the
hourly time step and interpolated over the catchment using an inverse-distance-weighting method. The COMEPHORE rainfall dataset and PET derived from interpolated temperature are the best observation-based estimates in this area and are therefore considered the reference observational datasets in this paper.

Numerous methods exist to compute PET from climatic variables for hydrological modeling perspectives (Oudin et al., 2005). Here, we compute PET using the Hargreaves–Samani (HS) method (Hargreaves and Samani, 1982). We chose a reliable, widespread applied method that requires the minimum amount of climate variables at the daily time step. Daily PET is then disaggregated to the hourly time step using a standard distribution curve as done in Lobligeois (2014). We applied the same methodology for simulated temperature.

## 2.4 Hydrological models

Two hydrological models with different physical concepts were used in this framework. This choice was made to consider the potential uncertainty related to hydrological modeling that can affect hydrological projections (see, e.g., Lemaitre-Basset et al., 2021, which discusses this issue for the neighboring Hérault catchment).

The GR5H model is a lumped, conceptual rainfall-runoff model that runs hourly (Ficchì et al., 2019). Based on several conceptual reservoirs, such as a soil moisture accounting reservoir, and a unit hydrograph, this model uses catchment-aggregated hourly precipitation and potential evapotranspiration data to simulate hourly discharge. The GR5H model takes into account the interception of rainfall by vegetation, which was proven important for flood events (Ficchì et al., 2019). The GR5H model parameters were calibrated against observed discharge using the Nash and Sutcliffe efficiency (NSE) objective function. This model or close versions belonging to the so-called GR family of hydrological models have been used both for flood simulation (Ficchì et al., 2019; Astagneau et al., 2021) and climate change applications (Chauveau et al., 2013; Lemaitre-Basset et al., 2021). The GR5H model was run using the open-source airGR R package (Coron et al., 2017, 2020), which also provides the parameter calibration algorithm that was used.

The Ensemble Framework For Flash Flood Forecasting (EF5) is a software for distributed water balance modeling (Flamig et al., 2020), including different schemes for runoff production and routing. As part of EF5, we used the Coupled Routing and Excess STorage (CREST) model and the kinematic wave routing scheme, at the hourly time step. CREST is a fully distributed and process-based hydrological model. It can be defined as a hybrid between a conceptual and a physics-based model (Wang et al., 2011). All hydrological processes, such as runoff production, evapotranspiration and sub-grid cell routing are computed at each grid cell. CREST is composed of two excess storage reservoirs,

one for interception by the vegetation canopy and one representing a layer of soil. For each cell, runoff and infiltration are separated using a variable infiltration curve. Therefore, this model takes into account the two main runoff production mechanisms: saturation excess and infiltration excess. The subsurface routing is done with a linear reservoir model. Total runoff, composed of surface and sub-surface runoff water, is then routed to the outlet following the orography provided by a digital elevation model (DEM) with the kinematic wave routing scheme. Actual evapotranspiration is determined in the model from PET input and the water content of the cell. The CREST model is composed of 13 parameters: 6 for runoff production and 7 for the routing. Here we will refer to this adaptation of CREST in EF5 simply as CREST for succinctness. The DiffeRental Evolution Adaptive Metropolis (DREAM) scheme is used for automatic calibration to estimate the best parameter set (Vrugt et al., 2009). A complete description of parameters is provided in Flamig et al. (2020). CREST is a model initially developed to respond to the need of forecasting floods at the global scale (Wang et al., 2011) but is perfectly suitable to simulate flash floods (Flamig et al., 2020) through EF5. Indeed, CREST has been used to study extreme hydrological events, for example, to reproduce floods from a major hurricane event (Li et al., 2021) or floods and flash floods in a warmer future (Li et al., 2022b, a). In this study, topographic data from version 1.1 of the HydroSHEDS database are used for CREST. Resolution of the DEM is 15 arcsec; hence, for this latitude it is around 300 m in longitude by 450 m in latitude. Drainage direction maps and flow accumulation maps are then produced using the QGIS software and packages.

The CREST and GR5H hydrological models have been calibrated using the hourly observed discharge from 2002–2018 and using the COMEPHORE hourly rainfall dataset and PET computed from observed air temperatures. For GR5H, all the model parameters have been calibrated. For CREST, most parameters have been fixed (Li et al., 2022a), and the calibration has been performed on a few sensitive parameters. The NSE criteria are used as an objective function for the calibration process. We initialized the models during a 1-year warmup period before the starting date. Both hydrological models have been evaluated using the following metrics (see Sect. 4.2) over the 2002–2018 period:

- Nash and Sutcliffe efficiency (Nash and Sutcliffe, 1970);

- KGE (Gupta et al., 2009);

- bias on mean flows;

- bias on quantile 99.9;

- bias on peak over threshold (POT) distributions – we compared the mean of simulated POT distribution to the mean of observed POT distribution.
hourly time step and interpolated over the catchment using an inverse-distance-weighting method. The COMEPHORE rainfall dataset and PET derived from interpolated temperature are the best observation-based estimates in this area and are therefore considered the reference observational datasets in this paper.

Numerous methods exist to compute PET from climatic variables for hydrological modeling perspectives (Oudin et al., 2005). Here, we compute PET using the Hargreaves–Samani (HS) method (Hargreaves and Samani, 1982). We chose a reliable, widespread applied method that requires the minimum amount of climate variables at the daily time step. Daily PET is then disaggregated to the hourly time step using a standard distribution curve as done in Lobligeois (2014). We applied the same methodology for simulated temperature.

## 2.4 Hydrological models

Two hydrological models with different physical concepts were used in this framework. This choice was made to consider the potential uncertainty related to hydrological modeling that can affect hydrological projections (see, e.g., Lemaitre-Basset et al., 2021, which discusses this issue for the neighboring Hérault catchment).

The GR5H model is a lumped, conceptual rainfall-runoff model that runs hourly (Ficchì et al., 2019). Based on several conceptual reservoirs, such as a soil moisture accounting reservoir, and a unit hydrograph, this model uses catchment-aggregated hourly precipitation and potential evapotranspiration data to simulate hourly discharge. The GR5H model takes into account the interception of rainfall by vegetation, which was proven important for flood events (Ficchì et al., 2019). The GR5H model parameters were calibrated against observed discharge using the Nash and Sutcliffe efficiency (NSE) objective function. This model or close versions belonging to the so-called GR family of hydrological models have been used both for flood simulation (Ficchì et al., 2019; Astagneau et al., 2021) and climate change applications (Chauveau et al., 2013; Lemaitre-Basset et al., 2021). The GR5H model was run using the open-source airGR R package (Coron et al., 2017, 2020), which also provides the parameter calibration algorithm that was used.

The Ensemble Framework For Flash Flood Forecasting (EF5) is a software for distributed water balance modeling (Flamig et al., 2020), including different schemes for runoff production and routing. As part of EF5, we used the Coupled Routing and Excess STorage (CREST) model and the kinematic wave routing scheme, at the hourly time step. CREST is a fully distributed and process-based hydrological model. It can be defined as a hybrid between a conceptual and a physics-based model (Wang et al., 2011). All hydrological processes, such as runoff production, evapotranspiration and sub-grid cell routing are computed at each grid cell. CREST is composed of two excess storage reservoirs,

one for interception by the vegetation canopy and one representing a layer of soil. For each cell, runoff and infiltration are separated using a variable infiltration curve. Therefore, this model takes into account the two main runoff production mechanisms: saturation excess and infiltration excess. The subsurface routing is done with a linear reservoir model. Total runoff, composed of surface and sub-surface runoff water, is then routed to the outlet following the orography provided by a digital elevation model (DEM) with the kinematic wave routing scheme. Actual evapotranspiration is determined in the model from PET input and the water content of the cell. The CREST model is composed of 13 parameters: 6 for runoff production and 7 for the routing. Here we will refer to this adaptation of CREST in EF5 simply as CREST for succinctness. The DiffeRental Evolution Adaptive Metropolis (DREAM) scheme is used for automatic calibration to estimate the best parameter set (Vrugt et al., 2009). A complete description of parameters is provided in Flamig et al. (2020). CREST is a model initially developed to respond to the need of forecasting floods at the global scale (Wang et al., 2011) but is perfectly suitable to simulate flash floods (Flamig et al., 2020) through EF5. Indeed, CREST has been used to study extreme hydrological events, for example, to reproduce floods from a major hurricane event (Li et al., 2021) or floods and flash floods in a warmer future (Li et al., 2022b, a). In this study, topographic data from version 1.1 of the HydroSHEDS database are used for CREST. Resolution of the DEM is 15 arcsec; hence, for this latitude it is around 300 m in longitude by 450 m in latitude. Drainage direction maps and flow accumulation maps are then produced using the QGIS software and packages.

The CREST and GR5H hydrological models have been calibrated using the hourly observed discharge from 2002–2018 and using the COMEPHORE hourly rainfall dataset and PET computed from observed air temperatures. For GR5H, all the model parameters have been calibrated. For CREST, most parameters have been fixed (Li et al., 2022a), and the calibration has been performed on a few sensitive parameters. The NSE criteria are used as an objective function for the calibration process. We initialized the models during a 1-year warmup period before the starting date. Both hydrological models have been evaluated using the following metrics (see Sect. 4.2) over the 2002–2018 period:

- Nash and Sutcliffe efficiency (Nash and Sutcliffe, 1970);

- KGE (Gupta et al., 2009);

- bias on mean flows;

- bias on quantile 99.9;

- bias on peak over threshold (POT) distributions – we compared the mean of simulated POT distribution to the mean of observed POT distribution.
The same period (2002–2018) is kept to run the hydrologi-
cal models driven with retrospective climate simulations. For
the scenario datasets, using a 1-year warmup period, we com-
puted simulations over two epochs of 19 years for historical
(1987–2005) and future (2081–2099) periods.

3 Methods

**3.1 Bias correction**

Climate simulations and notably precipitation often show
significant biases that prevent their direct use in impact stud-
ies such as for hydrological impact modeling. Indeed, hydro-
logical models are calibrated over climatic conditions that
can differ strongly from the raw historical climate simula-
tions. The use of bias correction methods on climate simu-
lations is an open debate (Addor and Seibert, 2014; Huang
et al., 2014; Maraun, 2016). However, correction of biased
climate simulations is widely used for hydrological impact
studies and future projections (Reszler et al., 2018; Giorgi,
2019; Lucas-Picher et al., 2021).

In order to correct simulated climate data biases, we used
an univariate statistical bias correction method. CDF-t is a
statistical bias correction method specifically developed for
correcting biases of climate variables (Michelangeli et al.,
2009). The basis of this method is an extension of a quantile
mapping method that allows a change in distribution statis-
tics for corrected variables. Consequently, CDF-t is particu-
larly appropriate to correct future climate datasets in a non-
stationary climate (Michelangeli et al., 2009; Vrac et al.,
2012), and it is largely used to correct future climate pro-
jections for the sake of climate change impact studies. In this
study, we applied the CDF-t correction on hourly precipita-
tion and temperature variables. Due to the distinct season-
ality of precipitation and a strong spatial variability of pre-
cipitation in this catchment, we corrected simulated datasets
for each calendar month and over each grid cell separately.
To take into account differences in the ratio of wet and dry
hours, the Singularity Stochastic Removal (SSR) preprocess-
ing method is applied for precipitation simulations (Vrac
et al., 2016). Historical simulations are corrected against
the corresponding observed period (2000–2018). With the
common period of data between observations and histori-
cal AROME simulations being relatively short (8 years), we
chose to correct observations and the historical simulation
over two asynchronous periods of same length, respectively
2000–2019 and 1986–2005. This correction is then applied
over the end-of-century RCP8.5 projections (2080–2099).
All these operations are performed through the R package
SBCK (Robin, 2022). Raw and bias-corrected hourly precip-
itation patterns have been compared for some extreme rain-
fall events, and spatial correlation is little affected by the bias
correction (not shown). For the GR5H model, corrected cli-

mate outputs are then averaged over the Gardon d'Anduze
catchment.

**3.2 POT extraction**

This study focuses on floods. To extract floods from dis-
charge time series, two approaches are widely adopted: the
annual maximum flood (AMF) and a peak over thresh-
old (POT) extraction (Lang et al., 1999). The POT selection
method consists of the extraction of all flood peaks exceeding
a threshold. Contrary to the AMF method, the POT method
preserves the hydrological information especially in Mediter-
ranean catchments where several flash floods can occur every
year. Hence, we selected this method for flood extraction in
this paper. The POT method requires temporal independence
between flood peaks. To ensure this assumption, a declus-
tering algorithm has been developed and applied to evaluate
temporal dependencies between POTs, adapted from Lang
et al. (1999). The declustering applied herein considers that
two floods are independent if there is a minimum duration of
96 h between the two events and if the discharge between dif-
ferent events must drop below 75 % of the minimum value of
the flood peaks. Cunnane (1973) found that the POT method
minimizes the sampling variance for a threshold producing
on average 1.65 flood peaks per year, compared to the AMF.
To get a sufficient sample of simulated POT (in particular for
ALADIN), we fixed here the discharge threshold to get closer
to 2 floods per year for the observed discharge (correspond-
ing to a threshold equal to 265 m³ s⁻¹), after declustering.

**3.3 Flood characteristics**

For each event, several flood characteristics and their associ-
ated rainfall events are also analyzed. These metrics aim to
understand future changes in flood mechanisms as projected
by climate simulations. For instance, changes in flood peak
baseflow can give information on change in antecedent soil
moisture. The lag-time and flashiness indexes describe the
intensity and speed of the rise of the river flow, a signature
of potential catastrophic impacts (flash flood). At the same
time, characteristics of the rainfall events (duration, inten-
sity, maximum) in tandem with the flood characteristics help
elucidate the driving processes and causes of future changes
in the basin’s hydrometeorology. Rainfall events associated
with each POT (Sect. 3.2) are defined by a sequence of hourly
rainfall prior to the flood peak (i) interrupted by no more than
2 dry hours and (ii) yielding at least 30 mm to trigger the
hydrological event. These two conditions have been tested
for different values with observed datasets and have shown
very little sensitivity to defining the flood characteristics. The
rainfall thresholds are related to our knowledge of the river
basin dynamics and hydrological expertise. Table 2 summa-
rizes metrics names, meaning and computation details.
The same period (2002–2018) is kept to run the hydrologi-
cal models driven with retrospective climate simulations. For
the scenario datasets, using a 1-year warmup period, we com-
puted simulations over two epochs of 19 years for historical
(1987–2005) and future (2081–2099) periods.

3 Methods

**3.1 Bias correction**

Climate simulations and notably precipitation often show
significant biases that prevent their direct use in impact stud-
ies such as for hydrological impact modeling. Indeed, hydro-
logical models are calibrated over climatic conditions that
can differ strongly from the raw historical climate simula-
tions. The use of bias correction methods on climate simu-
lations is an open debate (Addor and Seibert, 2014; Huang
et al., 2014; Maraun, 2016). However, correction of biased
climate simulations is widely used for hydrological impact
studies and future projections (Reszler et al., 2018; Giorgi,
2019; Lucas-Picher et al., 2021).

In order to correct simulated climate data biases, we used
an univariate statistical bias correction method. CDF-t is a
statistical bias correction method specifically developed for
correcting biases of climate variables (Michelangeli et al.,
2009). The basis of this method is an extension of a quantile
mapping method that allows a change in distribution statis-
tics for corrected variables. Consequently, CDF-t is particu-
larly appropriate to correct future climate datasets in a non-
stationary climate (Michelangeli et al., 2009; Vrac et al.,
2012), and it is largely used to correct future climate pro-
jections for the sake of climate change impact studies. In this
study, we applied the CDF-t correction on hourly precipita-
tion and temperature variables. Due to the distinct season-
ality of precipitation and a strong spatial variability of pre-
cipitation in this catchment, we corrected simulated datasets
for each calendar month and over each grid cell separately.
To take into account differences in the ratio of wet and dry
hours, the Singularity Stochastic Removal (SSR) preprocess-
ing method is applied for precipitation simulations (Vrac
et al., 2016). Historical simulations are corrected against
the corresponding observed period (2000–2018). With the
common period of data between observations and histori-
cal AROME simulations being relatively short (8 years), we
chose to correct observations and the historical simulation
over two asynchronous periods of same length, respectively
2000–2019 and 1986–2005. This correction is then applied
over the end-of-century RCP8.5 projections (2080–2099).
All these operations are performed through the R package
SBCK (Robin, 2022). Raw and bias-corrected hourly precip-
itation patterns have been compared for some extreme rain-
fall events, and spatial correlation is little affected by the bias
correction (not shown). For the GR5H model, corrected cli-

mate outputs are then averaged over the Gardon d'Anduze
catchment.

**3.2 POT extraction**

This study focuses on floods. To extract floods from dis-
charge time series, two approaches are widely adopted: the
annual maximum flood (AMF) and a peak over thresh-
old (POT) extraction (Lang et al., 1999). The POT selection
method consists of the extraction of all flood peaks exceeding
a threshold. Contrary to the AMF method, the POT method
preserves the hydrological information especially in Mediter-
ranean catchments where several flash floods can occur every
year. Hence, we selected this method for flood extraction in
this paper. The POT method requires temporal independence
between flood peaks. To ensure this assumption, a declus-
tering algorithm has been developed and applied to evaluate
temporal dependencies between POTs, adapted from Lang
et al. (1999). The declustering applied herein considers that
two floods are independent if there is a minimum duration of
96 h between the two events and if the discharge between dif-
ferent events must drop below 75 % of the minimum value of
the flood peaks. Cunnane (1973) found that the POT method
minimizes the sampling variance for a threshold producing
on average 1.65 flood peaks per year, compared to the AMF.
To get a sufficient sample of simulated POT (in particular for
ALADIN), we fixed here the discharge threshold to get closer
to 2 floods per year for the observed discharge (correspond-
ing to a threshold equal to 265 m³ s⁻¹), after declustering.

**3.3 Flood characteristics**

For each event, several flood characteristics and their associ-
ated rainfall events are also analyzed. These metrics aim to
understand future changes in flood mechanisms as projected
by climate simulations. For instance, changes in flood peak
baseflow can give information on change in antecedent soil
moisture. The lag-time and flashiness indexes describe the
intensity and speed of the rise of the river flow, a signature
of potential catastrophic impacts (flash flood). At the same
time, characteristics of the rainfall events (duration, inten-
sity, maximum) in tandem with the flood characteristics help
elucidate the driving processes and causes of future changes
in the basin’s hydrometeorology. Rainfall events associated
with each POT (Sect. 3.2) are defined by a sequence of hourly
rainfall prior to the flood peak (i) interrupted by no more than
2 dry hours and (ii) yielding at least 30 mm to trigger the
hydrological event. These two conditions have been tested
for different values with observed datasets and have shown
very little sensitivity to defining the flood characteristics. The
rainfall thresholds are related to our knowledge of the river
basin dynamics and hydrological expertise. Table 2 summa-
rizes metrics names, meaning and computation details.
<table><caption>**Table 2.** Flood characteristics definition and details.</caption><thead><tr><th>Metric name</th><th>Definition</th><th>Equation</th></tr></thead><tbody><tr><td>n<sub>P</sub></td><td>Duration of rainfall event (h)</td><td></td></tr><tr><td>P<sub>max</sub></td><td>Maximum hourly precipitation of rainfall events associated with the flood (mm)</td><td>P<sub>max</sub> = max(P<sub>i</sub>)<br>i are temporal indices of rainfall event</td></tr><tr><td>P<sub>tot</sub></td><td>Rainfall event total amount (mm)</td><td>P<sub>tot</sub> = &sum;<sub>1</sub><sup>n<sub>P</sub></sup> P<sub>i</sub></td></tr><tr><td>B<sub>POT</sub></td><td>Baseflow value (m<sup>3</sup> s<sup>-1</sup>) at the flood peak time step. Baseflow time series are extracted from the R package hydroEvents with default filter values (Wasko and Guo, 2022)</td><td></td></tr><tr><td>R<sub>POT</sub></td><td>Baseflow contribution of the POT (%)</td><td>R<sub>POT</sub> = <sup>B<sub>POT</sub></sup>&frasl;<sub>Q<sub>POT</sub></sub></td></tr><tr><td>n<sub>Q</sub></td><td>Duration of flood event (h)</td><td>n<sub>Q</sub>: number of time steps exceeding the threshold</td></tr><tr><td>L<sub>T</sub></td><td>Lag time (h), reactivity (concentration time) of the catchment for the given hydro-meteorological event. Time difference between the rainfall event centroid and the POT. To remove artifacts, and for consistency issues, a temporal window of 24 h prior to the POT was chosen.</td><td>j<sub>centroid</sub> = <sup>&sum;<sub>q-24</sub><sup>q</sup> j<sub>i</sub>P<sub>j</sub></sup>&frasl;<sub>&sum;<sub>q-24</sub><sup>q</sup> P<sub>j</sub></sub><br>with<br>j: indices of flood event<br>q = j<sub>POT</sub> the index of POT<br>L<sub>T</sub> = q - j<sub>centroid</sub></td></tr><tr><td>F<sub>I</sub></td><td>Flashiness index (-): flashiness of flood determined by a combination of flood reactivity and magnitude, proxy of flood severity. Adapted from Li et al. (2022b).</td><td>F<sub>I</sub> = <sup>Q<sub>POT</sub> - B<sub>POT</sub></sup>&frasl;<sub>L<sub>T</sub></sub></td></tr></tbody></table>

# 4 Results and discussion

## 4.1 Added value of CPM at the catchment scale

We first analyze the climate datasets aggregated at the scale of the Gardon d'Anduze catchment. Figure 2 shows the annual cycle for daily minimum ($T_{min}$) and maximum ($T_{max}$) temperature and hourly precipitation before (raw) and after bias correction (BC-CDFT). AROME and ALADIN in retrospective mode (evaluation) show correct annual cycles despite an overestimation of summer temperature both for $T_{min}$ and $T_{max}$. Results in this area are in line with the recent evaluation of the ALADIN and AROME climate models (Lucas-Picher et al., 2023). The ALADIN RCM is generally colder than AROME, which can be explained by an effect of resolution. A strong cold bias (3–5 °C) is visible over all seasons for both models in historical scenarios over the period 1986–2005. This cannot be explained by climate variability or by climate change signals between the decades 1986–2005 and 2000–2018. This is a known cold bias of climate models driven by the GCM CNRM-CM5 (Vautard et al., 2021). Annual cycles for $T_{min}$ and $T_{max}$ for future projections

(RCP8.5) are similar to the evaluation period; i.e., climate change signals for temperature are almost of the same magnitude as the cold bias. As expected, this cold bias disappears after bias correction for both AROME and ALADIN historical scenarios. The bias-corrected temperatures under the RCP8.5 scenario strongly increase for all seasons. However, the strongest signal is seen for the summer months.

Annual cycles of precipitation are well reproduced for ALADIN and AROME for the evaluation period. Both models are a bit too wet in spring and too dry in summer, but the wet season (October to December) on simulations is clearly distinguishable and identical to COMEPHORE dataset (observations). The wet bias in spring can deeply affect soil moisture state in the hydrological models and therefore probably lead to a change in the behavior of the first floods occurring during the autumn months. The two climate models are able to reproduce the precipitation seasonality. There is no evidence of added value from the CPM on the simulation of the annual cycle of precipitation on this catchment. Results are similar for AROME and ALADIN for historical projections: spring and summer seasons are too wet. This causes a weaker annual cycle of precipitation. The wet fall season,
<table><caption>**Table 2.** Flood characteristics definition and details.</caption><thead><tr><th>Metric name</th><th>Definition</th><th>Equation</th></tr></thead><tbody><tr><td>n<sub>P</sub></td><td>Duration of rainfall event (h)</td><td></td></tr><tr><td>P<sub>max</sub></td><td>Maximum hourly precipitation of rainfall events associated with the flood (mm)</td><td>P<sub>max</sub> = max(P<sub>i</sub>)<br>i are temporal indices of rainfall event</td></tr><tr><td>P<sub>tot</sub></td><td>Rainfall event total amount (mm)</td><td>P<sub>tot</sub> = &sum;<sub>1</sub><sup>n<sub>P</sub></sup> P<sub>i</sub></td></tr><tr><td>B<sub>POT</sub></td><td>Baseflow value (m<sup>3</sup> s<sup>-1</sup>) at the flood peak time step. Baseflow time series are extracted from the R package hydroEvents with default filter values (Wasko and Guo, 2022)</td><td></td></tr><tr><td>R<sub>POT</sub></td><td>Baseflow contribution of the POT (%)</td><td>R<sub>POT</sub> = <sup>B<sub>POT</sub></sup>&frasl;<sub>Q<sub>POT</sub></sub></td></tr><tr><td>n<sub>Q</sub></td><td>Duration of flood event (h)</td><td>n<sub>Q</sub>: number of time steps exceeding the threshold</td></tr><tr><td>L<sub>T</sub></td><td>Lag time (h), reactivity (concentration time) of the catchment for the given hydro-meteorological event. Time difference between the rainfall event centroid and the POT. To remove artifacts, and for consistency issues, a temporal window of 24 h prior to the POT was chosen.</td><td>j<sub>centroid</sub> = <sup>&sum;<sub>q-24</sub><sup>q</sup> j<sub>Pj</sub></sup>&frasl;<sub>&sum;<sub>q-24</sub><sup>q</sup> P<sub>j</sub></sub><br>with<br>j: indices of flood event<br>q = j<sub>POT</sub> the index of POT<br>L<sub>T</sub> = q - j<sub>centroid</sub></td></tr><tr><td>F<sub>I</sub></td><td>Flashiness index (-): flashiness of flood determined by a combination of flood reactivity and magnitude, proxy of flood severity. Adapted from Li et al. (2022b).</td><td>F<sub>I</sub> = <sup>Q<sub>POT</sub> - B<sub>POT</sub></sup>&frasl;<sub>L<sub>T</sub></sub></td></tr></tbody></table>

# 4 Results and discussion

## 4.1 Added value of CPM at the catchment scale

We first analyze the climate datasets aggregated at the scale of the Gardon d'Anduze catchment. Figure 2 shows the annual cycle for daily minimum ($T_{min}$) and maximum ($T_{max}$) temperature and hourly precipitation before (raw) and after bias correction (BC-CDFT). AROME and ALADIN in retrospective mode (evaluation) show correct annual cycles despite an overestimation of summer temperature both for $T_{min}$ and $T_{max}$. Results in this area are in line with the recent evaluation of the ALADIN and AROME climate models (Lucas-Picher et al., 2023). The ALADIN RCM is generally colder than AROME, which can be explained by an effect of resolution. A strong cold bias (3–5 °C) is visible over all seasons for both models in historical scenarios over the period 1986–2005. This cannot be explained by climate variability or by climate change signals between the decades 1986–2005 and 2000–2018. This is a known cold bias of climate models driven by the GCM CNRM-CM5 (Vautard et al., 2021). Annual cycles for $T_{min}$ and $T_{max}$ for future projections

(RCP8.5) are similar to the evaluation period; i.e., climate change signals for temperature are almost of the same magnitude as the cold bias. As expected, this cold bias disappears after bias correction for both AROME and ALADIN historical scenarios. The bias-corrected temperatures under the RCP8.5 scenario strongly increase for all seasons. However, the strongest signal is seen for the summer months.

Annual cycles of precipitation are well reproduced for ALADIN and AROME for the evaluation period. Both models are a bit too wet in spring and too dry in summer, but the wet season (October to December) on simulations is clearly distinguishable and identical to COMEPHORE dataset (observations). The wet bias in spring can deeply affect soil moisture state in the hydrological models and therefore probably lead to a change in the behavior of the first floods occurring during the autumn months. The two climate models are able to reproduce the precipitation seasonality. There is no evidence of added value from the CPM on the simulation of the annual cycle of precipitation on this catchment. Results are similar for AROME and ALADIN for historical projections: spring and summer seasons are too wet. This causes a weaker annual cycle of precipitation. The wet fall season,
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Annual cycle of daily-aggregated minimum and maximum 2 m temperature ($T_{min}$ and $T_{max}$) and hourly precipitation for raw (a, c, e) and bias-corrected (b, d, f) climate datasets. A 30 d rolling mean has been applied in order to smooth the high-frequency natural variability.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Annual cycle of daily-aggregated minimum and maximum 2 m temperature ($T_{min}$ and $T_{max}$) and hourly precipitation for raw (a, c, e) and bias-corrected (b, d, f) climate datasets. A 30 d rolling mean has been applied in order to smooth the high-frequency natural variability.</figcaption></figure>
<table><caption>**Table 3.** Evaluation of raw and corrected simulated precipitation with respect to the COMEPHORE observed dataset.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">ALADIN</th><th colspan="2">AROME</th></tr><tr><th>raw</th><th>corrected</th><th>raw</th><th>corrected</th></tr></thead><tbody><tr><td>Bias on median (%)</td><td>-27.2</td><td>0.4</td><td>-16.5</td><td>3.9</td></tr><tr><td>Bias on Q<sub>95</sub> (%)</td><td>-40.4</td><td>1.3</td><td>-19.6</td><td>-3.4</td></tr><tr><td>Bias on Q<sub>99</sub> (%)</td><td>-45.9</td><td>5.3</td><td>-12.2</td><td>0</td></tr></tbody></table>

however, is correctly reproduced, but with too early a start and plateau. The CDF-t bias correction method is able to correct the annual cycle of precipitation. The dry season is therefore consistent with the reference. In terms of intensity, ALADIN corrected shows an increase in intensity of the wet season peak (> 0.4 mm h⁻¹). We do not find this signal with AROME after correction. There is a dry signal for AROME corrected for the summer months. Results show no signal of wet season lengthening between the historical period and the future RCP8.5 scenario.

In terms of precipitation distribution, for the evaluation (retrospective) simulations, AROME performs better than ALADIN, especially for the precipitation extremes (Fig. 3, Table 3). The most extreme events (> quantile 99.9) remain underestimated for AROME. Fumière et al. (2020), Caillaud et al. (2021) and Lucas-Picher et al. (2023) have already shown the added value of the AROME CPM compared to its driving RCM, ALADIN, for modeling extreme hourly precipitation. This result confirms these past studies at the scale of a small catchment in the Cévennes region where AROME shows a clear added value (Caillaud et al., 2021).

The future ALADIN projected rainfall is lower than that of AROME, for the whole distribution. Extreme projected precipitation for ALADIN is lower than for the historical AROME data, partially due to the persistent bias. However, the projected hourly precipitation shows an increase in extreme hourly rainfall (> 95th percentile) under a warmer climate (RCP8.5 projection) for both models. This signal is stronger for the most extreme hourly rainfall (> 99.9th percentile) and for the AROME simulation. This local-scale result is in agreement with the broader multi-model ensemble study of Pichelli et al. (2021), which compared an ensemble of CPMs and their driving RCMs over 10-year periods (historical and future). They found a consistent signal of hourly rainfall extremes over southern France between CPMs and RCMs for the wet season (fall). For the dry season, a slight decrease in the 99.9th percentile of hourly precipitation of CPMs is shown, consistent with the annual cycle projection of Fig. 2. The large increase in the hourly extreme simulated precipitation is maintained after the bias correction. The signal of the projected precipitation, after bias correction, is largely positive for all probabilities exceeding 95th percentiles for both models. While ALADIN shows a stronger increase for the 95th and 99th percentiles than those

<table><caption>**Table 4.** Changes in extreme hourly corrected precipitation (%) under the RCP8.5 scenario relative to historical simulation for AROME and ALADIN.</caption><thead><tr><th>Percentile</th><th>AROME</th><th>ALADIN</th></tr></thead><tbody><tr><td>95th</td><td>+4.1</td><td>+16.6</td></tr><tr><td>99th</td><td>+17.5</td><td>+36.5</td></tr><tr><td>99.9th</td><td>+52.4</td><td>+28</td></tr></tbody></table>

<table><caption>**Table 5.** Evaluation of hydrological model simulations against observed discharge (2002–2018). Results for general efficiency scores (NSE: Nash–Sutcliffe efficiency; KGE: Kling–Gupta efficiency) and relative biases on mean flows, high flows (quantile 99.9) and flood peaks (observed flood peak over threshold).</caption><thead><tr><th></th><th>NSE</th><th>KGE</th><th>Bias on mean flows (%)</th><th>Bias on Q<sub>99.9</sub> (%)</th><th>Bias on POT (%)</th></tr></thead><tbody><tr><td>GR5H</td><td>0.76</td><td>0.85</td><td>-2.28</td><td>-3.7</td><td>-9.21</td></tr><tr><td>CREST</td><td>0.54</td><td>0.68</td><td>8.49</td><td>19.9</td><td>-19.6</td></tr></tbody></table>

of AROME, AROME produces the most positive trend for the most extreme hourly corrected rainfall (see Table 4). Indeed, the most extreme projected hourly rainfall is therefore expected to reach hourly rainfalls that have never been observed at the catchment scale (> 40 mm h⁻¹).

## 4.2 Hydrological models evaluation

Table 5 presents a summary of the performance metrics of the hydrological models (GR5H and CREST) calculated over the evaluation period. GR5H reproduces well the observed discharge with KGE and NSE scores higher than 0.75. High flows and mean flows are correctly simulated even if they are slightly underestimated. The CREST performance is lower for all the metrics. It shows an acceptable global efficiency and an overestimation of both mean flows and high flows. Both hydrological models underestimate flood peaks.

Figure 4 shows the annual cycle of discharge at the outlet of the Gardon d'Anduze catchment. Both models are able to reproduce the annual cycle, in particular the high-flow season caused by HPEs and flood events. As seen on Table 5, CREST tends to produce a more excessive discharge response to rainfall than GR5H. GR5H tends to overestimate low flows for summer months, which is, however, not the focus of the present work. One should keep in mind that the two hydrological models have been calibrated using a different strategy; all parameters of GR5H have been calibrated while most of CREST parameters used a priori estimates, since it is a common strategy for fully distributed physically based models (Clark et al., 2017).
<table><caption>**Table 3.** Evaluation of raw and corrected simulated precipitation with respect to the COMEPHORE observed dataset.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">ALADIN</th><th colspan="2">AROME</th></tr><tr><th>raw</th><th>corrected</th><th>raw</th><th>corrected</th></tr></thead><tbody><tr><td>Bias on median (%)</td><td>-27.2</td><td>0.4</td><td>-16.5</td><td>3.9</td></tr><tr><td>Bias on Q<sub>95</sub> (%)</td><td>-40.4</td><td>1.3</td><td>-19.6</td><td>-3.4</td></tr><tr><td>Bias on Q<sub>99</sub> (%)</td><td>-45.9</td><td>5.3</td><td>-12.2</td><td>0</td></tr></tbody></table>

however, is correctly reproduced, but with too early a start and plateau. The CDF-t bias correction method is able to correct the annual cycle of precipitation. The dry season is therefore consistent with the reference. In terms of intensity, ALADIN corrected shows an increase in intensity of the wet season peak (> 0.4 mm h⁻¹). We do not find this signal with AROME after correction. There is a dry signal for AROME corrected for the summer months. Results show no signal of wet season lengthening between the historical period and the future RCP8.5 scenario.

In terms of precipitation distribution, for the evaluation (retrospective) simulations, AROME performs better than ALADIN, especially for the precipitation extremes (Fig. 3, Table 3). The most extreme events (> quantile 99.9) remain underestimated for AROME. Fumière et al. (2020), Caillaud et al. (2021) and Lucas-Picher et al. (2023) have already shown the added value of the AROME CPM compared to its driving RCM, ALADIN, for modeling extreme hourly precipitation. This result confirms these past studies at the scale of a small catchment in the Cévennes region where AROME shows a clear added value (Caillaud et al., 2021).

The future ALADIN projected rainfall is lower than that of AROME, for the whole distribution. Extreme projected precipitation for ALADIN is lower than for the historical AROME data, partially due to the persistent bias. However, the projected hourly precipitation shows an increase in extreme hourly rainfall (> 95th percentile) under a warmer climate (RCP8.5 projection) for both models. This signal is stronger for the most extreme hourly rainfall (> 99.9th percentile) and for the AROME simulation. This local-scale result is in agreement with the broader multi-model ensemble study of Pichelli et al. (2021), which compared an ensemble of CPMs and their driving RCMs over 10-year periods (historical and future). They found a consistent signal of hourly rainfall extremes over southern France between CPMs and RCMs for the wet season (fall). For the dry season, a slight decrease in the 99.9th percentile of hourly precipitation of CPMs is shown, consistent with the annual cycle projection of Fig. 2. The large increase in the hourly extreme simulated precipitation is maintained after the bias correction. The signal of the projected precipitation, after bias correction, is largely positive for all probabilities exceeding 95th percentiles for both models. While ALADIN shows a stronger increase for the 95th and 99th percentiles than those

<table><caption>**Table 4.** Changes in extreme hourly corrected precipitation (%) under the RCP8.5 scenario relative to historical simulation for AROME and ALADIN.</caption><thead><tr><th>Percentile</th><th>AROME</th><th>ALADIN</th></tr></thead><tbody><tr><td>95th</td><td>+4.1</td><td>+16.6</td></tr><tr><td>99th</td><td>+17.5</td><td>+36.5</td></tr><tr><td>99.9th</td><td>+52.4</td><td>+28</td></tr></tbody></table>

<table><caption>**Table 5.** Evaluation of hydrological model simulations against observed discharge (2002–2018). Results for general efficiency scores (NSE: Nash–Sutcliffe efficiency; KGE: Kling–Gupta efficiency) and relative biases on mean flows, high flows (quantile 99.9) and flood peaks (observed flood peak over threshold).</caption><thead><tr><th></th><th>NSE</th><th>KGE</th><th>Bias on mean flows (%)</th><th>Bias on Q<sub>99.9</sub> (%)</th><th>Bias on POT (%)</th></tr></thead><tbody><tr><td>GR5H</td><td>0.76</td><td>0.85</td><td>-2.28</td><td>-3.7</td><td>-9.21</td></tr><tr><td>CREST</td><td>0.54</td><td>0.68</td><td>8.49</td><td>19.9</td><td>-19.6</td></tr></tbody></table>

of AROME, AROME produces the most positive trend for the most extreme hourly corrected rainfall (see Table 4). Indeed, the most extreme projected hourly rainfall is therefore expected to reach hourly rainfalls that have never been observed at the catchment scale (> 40 mm h⁻¹).

## 4.2 Hydrological models evaluation

Table 5 presents a summary of the performance metrics of the hydrological models (GR5H and CREST) calculated over the evaluation period. GR5H reproduces well the observed discharge with KGE and NSE scores higher than 0.75. High flows and mean flows are correctly simulated even if they are slightly underestimated. The CREST performance is lower for all the metrics. It shows an acceptable global efficiency and an overestimation of both mean flows and high flows. Both hydrological models underestimate flood peaks.

Figure 4 shows the annual cycle of discharge at the outlet of the Gardon d'Anduze catchment. Both models are able to reproduce the annual cycle, in particular the high-flow season caused by HPEs and flood events. As seen on Table 5, CREST tends to produce a more excessive discharge response to rainfall than GR5H. GR5H tends to overestimate low flows for summer months, which is, however, not the focus of the present work. One should keep in mind that the two hydrological models have been calibrated using a different strategy; all parameters of GR5H have been calibrated while most of CREST parameters used a priori estimates, since it is a common strategy for fully distributed physically based models (Clark et al., 2017).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Distribution of wet hourly rainfall (> 0.1 mm h⁻¹) for raw (a, c) and bias-corrected (b, d) precipitation datasets. Panels (a) and (b) show the retrospective simulations (2000–2018). Panels (c) and (d) depict the historical (1986–2005) and future (2080–2099) simulations under the RCP8.5 scenario. COMEPHORE observed hourly rainfall is appended to all panels (2000–2018). Probabilities under the abscissa axis are shown under a Gumbel transformation.</figcaption></figure>

These results must be moderated looking at Fig. 5, which shows the cumulative distribution of the observed discharge along with the GR5H and CREST simulated discharge for the evaluation period. The study indeed focuses on the most intense flood events in this catchment, i.e., flood peak above 265 m³ s⁻¹, leading to two floods per year on average. Biases in CREST simulations (Table 5, Fig. 4) are not neces-

sarily reflected in the flood distribution since both models manage to reproduce the observed flood distribution on this small catchment. We can see a slight underestimation of the most intense flood by both hydrological models. The most severe flood corresponds to the major flood event of September 2002, one of the most damaging flash floods recorded in France (Delrieu et al., 2005; Vinet, 2008). Even if the return
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Distribution of wet hourly rainfall (> 0.1 mm h⁻¹) for raw (a, c) and bias-corrected (b, d) precipitation datasets. Panels (a) and (b) show the retrospective simulations (2000–2018). Panels (c) and (d) depict the historical (1986–2005) and future (2080–2099) simulations under the RCP8.5 scenario. COMEPHORE observed hourly rainfall is appended to all panels (2000–2018). Probabilities under the abscissa axis are shown under a Gumbel transformation.</figcaption></figure>

These results must be moderated looking at Fig. 5, which shows the cumulative distribution of the observed discharge along with the GR5H and CREST simulated discharge for the evaluation period. The study indeed focuses on the most intense flood events in this catchment, i.e., flood peak above 265 m³ s⁻¹, leading to two floods per year on average. Biases in CREST simulations (Table 5, Fig. 4) are not neces-

sarily reflected in the flood distribution since both models manage to reproduce the observed flood distribution on this small catchment. We can see a slight underestimation of the most intense flood by both hydrological models. The most severe flood corresponds to the major flood event of September 2002, one of the most damaging flash floods recorded in France (Delrieu et al., 2005; Vinet, 2008). Even if the return
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Annual cycle of discharge for observation, GR5H and CREST evaluation simulations over the 2002–2018 period. Multiyear regime of 8 d averaged flows at Anduze (Gardon d'Anduze catchment).</figcaption></figure>

period of this observed flood exceeds 50 years, this outlier value of the distribution of observed POT has to be carefully interpreted. The peak discharge value of a flood of this magnitude is likely inaccurate because of large uncertainties related to the measurement of the water level, the extrapolation from the rating curve, and possible modifications of river bed topography and flow dynamics (Neppel et al., 2010). However, in terms of flood frequency, the number of POTs differs between the two models. While CREST simulates on average 3.1 floods per year, GR5H is closer to observation with on average 1.5 floods per year.

## 4.3 Reproducing floods with the climate datasets

The flood distributions of the AROME- and ALADIN-driven simulations with GR5H and CREST are presented in Fig. 6. The green line on the Fig. 6 represents the hydrological simulations forced by the COMEPHORE observed dataset that has been used for the calibration and could be considered as the reference simulation. The AROME and ALADIN datasets are retrospective simulations (evaluation) with (CDF-t) and without bias correction. We used the same threshold as in Fig. 5, with the discharge value (265 m³ s⁻¹) leading to two observed floods per year after the declustering. Above all, only uncorrected retrospective climate simulations can be used in the scope of assessing the performance of climate models for reproducing floods. Firstly, Fig. 6 shows a large underestimation of POT distributions by CREST and GR5H. The hydrological models forced with the AROME

CPM reproduce flood intensity better than when forced with ALADIN. This can be observed by the shape of the distributions, which is almost flat for ALADIN. Then, the ALADIN-driven flood distributions fail to reproduce the observed flood frequency with 0.4 and 0.9 floods per year for GR5H and CREST, respectively, compared to AROME, which simulates an acceptable number of floods (1.2 for GR5H and 2.3 for CREST). Consequently, AROME seems more reliable than ALADIN in simulating floods with both hydrological models. The results shown in Fig. 6 confirm CREST over-reactivity with a higher number of POTs simulated than for GR5H. This model behavior does not impact the flood intensity. Conversely, discharges in the upper half of the flood peak distribution (cumulative frequency > 0.5) are slightly lower for CREST than for GR5H.

After the CDF-t bias correction, all POT distributions are closer to the hydrological simulation forced by the observed data (green curve). All simulated datasets are compared here with the simulated discharge driven by the observed precipitation (green curve). The ALADIN-driven hydrological simulation shows a slightly better POT distribution than the AROME-driven one for both models, even if the most intense flood event (September 2002) is better simulated with AROME. An improvement in flood frequency modeling is noticed after bias correction for all simulations. However, the CREST simulations still have a positive bias for flood frequency, with higher values than the COMEPHORE-driven simulation (2.9 and 3.3 floods per year for AROME and ALADIN, respectively). To summarize, the added value found
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Annual cycle of discharge for observation, GR5H and CREST evaluation simulations over the 2002–2018 period. Multiyear regime of 8 d averaged flows at Anduze (Gardon d'Anduze catchment).</figcaption></figure>

period of this observed flood exceeds 50 years, this outlier value of the distribution of observed POT has to be carefully interpreted. The peak discharge value of a flood of this magnitude is likely inaccurate because of large uncertainties related to the measurement of the water level, the extrapolation from the rating curve, and possible modifications of river bed topography and flow dynamics (Neppel et al., 2010). However, in terms of flood frequency, the number of POTs differs between the two models. While CREST simulates on average 3.1 floods per year, GR5H is closer to observation with on average 1.5 floods per year.

## 4.3 Reproducing floods with the climate datasets

The flood distributions of the AROME- and ALADIN-driven simulations with GR5H and CREST are presented in Fig. 6. The green line on the Fig. 6 represents the hydrological simulations forced by the COMEPHORE observed dataset that has been used for the calibration and could be considered as the reference simulation. The AROME and ALADIN datasets are retrospective simulations (evaluation) with (CDF-t) and without bias correction. We used the same threshold as in Fig. 5, with the discharge value (265 m³ s⁻¹) leading to two observed floods per year after the declustering. Above all, only uncorrected retrospective climate simulations can be used in the scope of assessing the performance of climate models for reproducing floods. Firstly, Fig. 6 shows a large underestimation of POT distributions by CREST and GR5H. The hydrological models forced with the AROME

CPM reproduce flood intensity better than when forced with ALADIN. This can be observed by the shape of the distributions, which is almost flat for ALADIN. Then, the ALADIN-driven flood distributions fail to reproduce the observed flood frequency with 0.4 and 0.9 floods per year for GR5H and CREST, respectively, compared to AROME, which simulates an acceptable number of floods (1.2 for GR5H and 2.3 for CREST). Consequently, AROME seems more reliable than ALADIN in simulating floods with both hydrological models. The results shown in Fig. 6 confirm CREST over-reactivity with a higher number of POTs simulated than for GR5H. This model behavior does not impact the flood intensity. Conversely, discharges in the upper half of the flood peak distribution (cumulative frequency > 0.5) are slightly lower for CREST than for GR5H.

After the CDF-t bias correction, all POT distributions are closer to the hydrological simulation forced by the observed data (green curve). All simulated datasets are compared here with the simulated discharge driven by the observed precipitation (green curve). The ALADIN-driven hydrological simulation shows a slightly better POT distribution than the AROME-driven one for both models, even if the most intense flood event (September 2002) is better simulated with AROME. An improvement in flood frequency modeling is noticed after bias correction for all simulations. However, the CREST simulations still have a positive bias for flood frequency, with higher values than the COMEPHORE-driven simulation (2.9 and 3.3 floods per year for AROME and ALADIN, respectively). To summarize, the added value found
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Cumulative distribution functions (CDFs) of observed and simulated flood peaks over the evaluation period (2002–2018). Both models have been calibrated and run with observed precipitation (COMEPHORE) and PET derived from the observed temperature. The threshold is a fixed discharge value giving two floods per year from the observed discharge (265 m³ s⁻¹).</figcaption></figure>

for the CPM compared to the RCM in extreme precipitation in previous sections seems to be transmitted for flash flood simulation. Bias correction reduces the difference between climate models, with no remnant bias on POT distribution. The choice of the hydrological model therefore does not seem to impact the results between the forcing climate datasets.

## 4.4 Climate change (flood intensity and frequency)

This section aims to provide an overview of the flood signal suggested by future climate projections and whether this projected evolution is impacted by the different hydrological models and climate models used. We now analyze the flood distributions from the hydrological models forced with the AROME and ALADIN climate simulations under the historical and the future RCP8.5 scenario (Fig. 7). In the first place, the two hydrological models show flood peak discharge weaker for ALADIN than for AROME. CREST simulates higher floods for historical and future projections than GR5H. The shape of the distribution of POT simulated with CREST is less flat than for GR5H distribution, reflecting a behavior similar to a Pareto distribution, hence a tail of the distribution associated with more extreme, i.e., rare events. The number of floods is higher for the CREST-driven hydrological simulations than GR5H ones. These simulations can

reach a flood frequency exceeding four floods per year on historical AROME- and ALADIN-driven discharge simulations.

Flood distributions from the bias-corrected historical and future projections show a good consistency between hydrological models, but higher differences among the climate simulations. Figure 7 highlights a major difference between the AROME CPM and its driving RCM, ALADIN. While the ALADIN-driven simulations indicate for both hydrological models a generalized increase in magnitude of floods, the AROME-driven simulations show a different signal depending on the probability of occurrence. Moderate floods are projected to be weaker in a warmer future when using AROME, but there is a positive increase for the most extreme floods. The threshold related to this change in signal seems to be located between 0.7 and 0.75 for both hydrological models; i.e., 25% to 30% of the most extreme floods are projected to be stronger in a future climate. Negative changes for CREST are less pronounced than for GR5H, but on the contrary, positive changes above this threshold are stronger, especially for the most extreme projected flood events.

The different changes in moderate and large floods could be explained by a decrease in future soil moisture, which can compensate for the increase in heavy precipitation for small to moderate floods. This result is in agreement with the studies of Brunner et al. (2021) and Wasko et al. (2023). This is
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Cumulative distribution functions (CDFs) of observed and simulated flood peaks over the evaluation period (2002–2018). Both models have been calibrated and run with observed precipitation (COMEPHORE) and PET derived from the observed temperature. The threshold is a fixed discharge value giving two floods per year from the observed discharge (265 m³ s⁻¹).</figcaption></figure>

for the CPM compared to the RCM in extreme precipitation in previous sections seems to be transmitted for flash flood simulation. Bias correction reduces the difference between climate models, with no remnant bias on POT distribution. The choice of the hydrological model therefore does not seem to impact the results between the forcing climate datasets.

## 4.4 Climate change (flood intensity and frequency)

This section aims to provide an overview of the flood signal suggested by future climate projections and whether this projected evolution is impacted by the different hydrological models and climate models used. We now analyze the flood distributions from the hydrological models forced with the AROME and ALADIN climate simulations under the historical and the future RCP8.5 scenario (Fig. 7). In the first place, the two hydrological models show flood peak discharge weaker for ALADIN than for AROME. CREST simulates higher floods for historical and future projections than GR5H. The shape of the distribution of POT simulated with CREST is less flat than for GR5H distribution, reflecting a behavior similar to a Pareto distribution, hence a tail of the distribution associated with more extreme, i.e., rare events. The number of floods is higher for the CREST-driven hydrological simulations than GR5H ones. These simulations can

reach a flood frequency exceeding four floods per year on historical AROME- and ALADIN-driven discharge simulations.

Flood distributions from the bias-corrected historical and future projections show a good consistency between hydrological models, but higher differences among the climate simulations. Figure 7 highlights a major difference between the AROME CPM and its driving RCM, ALADIN. While the ALADIN-driven simulations indicate for both hydrological models a generalized increase in magnitude of floods, the AROME-driven simulations show a different signal depending on the probability of occurrence. Moderate floods are projected to be weaker in a warmer future when using AROME, but there is a positive increase for the most extreme floods. The threshold related to this change in signal seems to be located between 0.7 and 0.75 for both hydrological models; i.e., 25% to 30% of the most extreme floods are projected to be stronger in a future climate. Negative changes for CREST are less pronounced than for GR5H, but on the contrary, positive changes above this threshold are stronger, especially for the most extreme projected flood events.

The different changes in moderate and large floods could be explained by a decrease in future soil moisture, which can compensate for the increase in heavy precipitation for small to moderate floods. This result is in agreement with the studies of Brunner et al. (2021) and Wasko et al. (2023). This is
<figure><img src="image_8.png" /><figcaption>**Figure 6.** POT CDF for GR5H (a, b) and CREST (c, d) forced by the observed and retrospective climate simulations (2001–2018). POT from raw climate datasets is on panels (a) and (c), and bias-corrected climate datasets are on panels (b) and (d). The threshold is the same as in Fig. 5.</figcaption></figure>

consistent as well with Tramblay et al. (2023) and Bertola et al. (2021), who showed the importance of antecedent soil moisture in modulating changes in floods. This threshold behavior is not present for the ALADIN-driven flood simulations. Indeed, the signal for the ALADIN flood projections is positive for almost all the flood distribution, except for the weakest floods where no clear signal is projected. Both bias-corrected ALADIN and AROME flood projection distributions show a strong increasing signal for the distribution tail. ALADIN-driven simulated floods in a future climate reach higher peak values than AROME when simulated by the GR5H model. For CREST, the distribution tail of future floods for AROME and ALADIN are of the same order of magnitude.

The different behavior between the AROME- and ALADIN-driven flood simulations questions the reliability of low-resolution RCMs for hydrological impact studies related to extreme events. The poor representation of topography along with the parameterization of deep convection for RCMs leads to strong biases on HPE's intensity and temporal distribution. Indeed, extreme rainfall events for ALADIN are generally composed of long-lasting moderate hourly rainfall rather than a more realistic convective precipitation peak. The bias correction method works on each individual hourly time step, but it does not influence the temporal distribution of rainfall events (i.e., hyetograph shape), leading to an overcorrection of the HPEs and probably to excessive hydrological reactions (not shown here). This strong bias correction
<figure><img src="image_10.png" /><figcaption>**Figure 6.** POT CDF for GR5H (a, b) and CREST (c, d) forced by the observed and retrospective climate simulations (2001–2018). POT from raw climate datasets is on panels (a) and (c), and bias-corrected climate datasets are on panels (b) and (d). The threshold is the same as in Fig. 5.</figcaption></figure>

consistent as well with Tramblay et al. (2023) and Bertola et al. (2021), who showed the importance of antecedent soil moisture in modulating changes in floods. This threshold behavior is not present for the ALADIN-driven flood simulations. Indeed, the signal for the ALADIN flood projections is positive for almost all the flood distribution, except for the weakest floods where no clear signal is projected. Both bias-corrected ALADIN and AROME flood projection distributions show a strong increasing signal for the distribution tail. ALADIN-driven simulated floods in a future climate reach higher peak values than AROME when simulated by the GR5H model. For CREST, the distribution tail of future floods for AROME and ALADIN are of the same order of magnitude.

The different behavior between the AROME- and ALADIN-driven flood simulations questions the reliability of low-resolution RCMs for hydrological impact studies related to extreme events. The poor representation of topography along with the parameterization of deep convection for RCMs leads to strong biases on HPE's intensity and temporal distribution. Indeed, extreme rainfall events for ALADIN are generally composed of long-lasting moderate hourly rainfall rather than a more realistic convective precipitation peak. The bias correction method works on each individual hourly time step, but it does not influence the temporal distribution of rainfall events (i.e., hyetograph shape), leading to an overcorrection of the HPEs and probably to excessive hydrological reactions (not shown here). This strong bias correction
<figure><img src="image_9.png" /><figcaption>**Figure 7.** POT distributions for GR5H (a, b) and CREST (c, d) forced by historical and future climate simulations. POTs from raw climate datasets are on panels (a) and (c), and those from bias-corrected climate datasets are on panels (b) and (d). The threshold is the same as for Fig. 5.</figcaption></figure>

probably prevents ALADIN from adequately simulating future changes on local processes that are highlighted by the AROME CPM. After bias correction, projected changes in flood frequency also depend on the forcing climate simulation. While AROME shows no change in flood frequency, ALADIN simulates a large increase in the number of floods in future climatic conditions. This trend remains consistent among the two hydrological models, with only little differences between CREST and GR5H simulations (Fig. 7).

## 4.5 Climate change impact on flood characteristics

We analyzed the flood and associated rainfall event characteristics (Fig. 8) simulated by historical and future bias-

corrected climate simulations. Every box plot is made of all values of a specific metric relative to all extracted flood events or the associated rainfall events for one dataset. Firstly, most simulated datasets show a negative future signal for the baseflow component of future floods. Only the GR5H model driven by ALADIN shows a little increase in the median, but this signal is not clear because the distribution becomes wider for future baseflows. The same future positive signal can be found on the ratio metric: all datasets yield a negative trend, reflecting an increase in the runoff part of the streamflow during floods. There is no clear signal about the changes in the length of precipitation events ($n_p$) and the total precipitation ($P_{tot}$) events associated with floods. However, the maximum hourly precipitation of the largest rainfall
<figure><img src="image_11.png" /><figcaption>**Figure 7.** POT distributions for GR5H (a, b) and CREST (c, d) forced by historical and future climate simulations. POTs from raw climate datasets are on panels (a) and (c), and those from bias-corrected climate datasets are on panels (b) and (d). The threshold is the same as for Fig. 5.</figcaption></figure>

probably prevents ALADIN from adequately simulating future changes on local processes that are highlighted by the AROME CPM. After bias correction, projected changes in flood frequency also depend on the forcing climate simulation. While AROME shows no change in flood frequency, ALADIN simulates a large increase in the number of floods in future climatic conditions. This trend remains consistent among the two hydrological models, with only little differences between CREST and GR5H simulations (Fig. 7).

## 4.5 Climate change impact on flood characteristics

We analyzed the flood and associated rainfall event characteristics (Fig. 8) simulated by historical and future bias-

corrected climate simulations. Every box plot is made of all values of a specific metric relative to all extracted flood events or the associated rainfall events for one dataset. Firstly, most simulated datasets show a negative future signal for the baseflow component of future floods. Only the GR5H model driven by ALADIN shows a little increase in the median, but this signal is not clear because the distribution becomes wider for future baseflows. The same future positive signal can be found on the ratio metric: all datasets yield a negative trend, reflecting an increase in the runoff part of the streamflow during floods. There is no clear signal about the changes in the length of precipitation events ($n_p$) and the total precipitation ($P_{tot}$) events associated with floods. However, the maximum hourly precipitation of the largest rainfall
event preceding the floods is projected to strongly increase. This result is coherent with Sect. 4.1 and confirms that hourly precipitation extremes can yield severe floods in this basin. The most intense increases seem to concern ALADIN-driven simulations with shifts in distribution exceeding 25 % (the median in historical simulations corresponds to the first quartile in the future). These results strengthen the confidence of the hypothesis of the decrease in soil moisture, leading to the threshold effect in AROME-forced simulations. In this study we do not explicitly simulate soil moisture, but the evolution of the baseflow can be considered as a proxy for the changes in soil moisture (Massari et al., 2023). The negative trend in the baseflow compensates for the increase in precipitation extremes until a threshold is reached, when the most intense hourly rainfall exceeds the infiltration capacity.

For the ALADIN-driven simulations, the constant increase in POT (Figs. 7 and 8) is a combination of two processes. Firstly, decreases in baseflow are less pronounced for ALADIN than for AROME, in particular for the GR5H hydrological model, reflecting a smaller infiltration capacity and the prevention of the damping role of soils in a warmer future. Secondly, as explained above, this trend is likely an artifact of the bias correction causing stronger HPEs in a warmer climate. Indeed, this can be highlighted in Fig. 8, where $P_{tot}$ shows a little increase for the ALADIN-driven simulations and a slight decrease in the medians of the AROME rainfall events. Outliers of $P_{tot}$ reach the highest values for the ALADIN-driven simulations. The length of future floods ($n_Q$) decreases for all simulations except for GR5H ALADIN, which shows a slight increase in the median and a lower spread for future scenarios. The lag time (LT) is projected to decrease for all simulations. This consistent signal of a shorter period between rainfall centroid and flood peak indicates a projected increase in the flood flashiness in this basin (FI). In more detail, the median of the flashiness index, representing reactivity and intensity of floods, is projected to increase for all simulations. The smallest increase in the flashiness index is shown for GR5H-AROME, while the other simulations show noteworthy positive trends and higher spreads. Some outliers reach extremely high values in the future projections, warning of potentially rare, but very extreme, flood events occurring in the future. This result is in agreement with the study of Li et al. (2022b), who found an increase in flash flood potential over the USA in the high-end emission scenario, notably in southern regions that have a wet convective season such as this Mediterranean catchment. However, the projected changes over this catchment should be interpreted with caution as they come from a single pair of RCM and CPM simulations. With these preliminary results, the different impacts of climate change on flood characteristics highlight the fine-scale benefits of CPM in simulating underlying hydrological processes. The robustness of the climate change impact on flash floods still needs to be confirmed with a comprehensive study that includes an uncertainty assessment.

## 5 Conclusions

Given the devastating consequences of Mediterranean floods, it is necessary to project whether the recent increase in precipitation extremes is going to continue in a warmer climate and have impacts on flood severity. Until now, flood projections have been based on regional climate models that cannot accurately simulate precipitation extremes that are yet the main factor causing these floods. In the last 10 years, emerging regional convection-permitting climate models using resolutions of a few kilometers show encouraging results in the simulations of short-duration precipitation extremes, opening the door to enhanced confidence and realism in future flood projections.

This study compared two regional climate model simulations used as inputs of two hydrological models to evaluate the possible climate change impacts on floods in a Mediterranean basin. The AROME convection-permitting climate model (CPM) with a 2.5 km spatial resolution has been compared to the ALADIN model with a 12 km spatial resolution. The evaluation of climate simulations shows similar results for both models regarding the reproduction of the annual cycles of temperature and precipitation. There is no added value of using the CPM for the representation of the seasonality of temperature and precipitation. Historical climate simulations are globally too cold with a wet bias for spring and summer. ALADIN and AROME both projected hotter and drier summers in the future but no drastic changes in the wet season duration and intensity, except a weak increase for ALADIN for the wet season precipitation peak. The added value of the CPM can be clearly seen on rainfall simulation before bias correction, with a much better representation of extremes with AROME compared to ALADIN, with the latter showing a strong underestimation. However, the bias correction reduces the difference between models since they are both corrected to match the observations. Both models project an increase in hourly precipitation under the RCP8.5 scenario. That signal is stronger for events above the 99.9th percentile than more moderate events above the 95th percentile, with a stronger signal for AROME.

Yet, both climate simulations required a bias correction to reproduce the observed discharge and notably flood events. Similar simulations of flood events have been obtained with the two hydrological models considered, a lumped conceptual model, GR5H, and a spatially distributed, process-based model, CREST, with slightly better results with the GR5H model. Very similar future projections have been obtained with the two hydrological models, highlighting the robustness of the results given the two different types of model structures. In terms of floods, the hydrological simulations driven by the climate model outputs showed contrasted discharge projections, with a general increase in the flood hazard using the ALADIN RCM and, on the contrary, an increase only for the largest floods using the AROME CPM. This indicates that the type of climate model can strongly
event preceding the floods is projected to strongly increase. This result is coherent with Sect. 4.1 and confirms that hourly precipitation extremes can yield severe floods in this basin. The most intense increases seem to concern ALADIN-driven simulations with shifts in distribution exceeding 25 % (the median in historical simulations corresponds to the first quartile in the future). These results strengthen the confidence of the hypothesis of the decrease in soil moisture, leading to the threshold effect in AROME-forced simulations. In this study we do not explicitly simulate soil moisture, but the evolution of the baseflow can be considered as a proxy for the changes in soil moisture (Massari et al., 2023). The negative trend in the baseflow compensates for the increase in precipitation extremes until a threshold is reached, when the most intense hourly rainfall exceeds the infiltration capacity.

For the ALADIN-driven simulations, the constant increase in POT (Figs. 7 and 8) is a combination of two processes. Firstly, decreases in baseflow are less pronounced for ALADIN than for AROME, in particular for the GR5H hydrological model, reflecting a smaller infiltration capacity and the prevention of the damping role of soils in a warmer future. Secondly, as explained above, this trend is likely an artifact of the bias correction causing stronger HPEs in a warmer climate. Indeed, this can be highlighted in Fig. 8, where $P_{tot}$ shows a little increase for the ALADIN-driven simulations and a slight decrease in the medians of the AROME rainfall events. Outliers of $P_{tot}$ reach the highest values for the ALADIN-driven simulations. The length of future floods ($n_Q$) decreases for all simulations except for GR5H ALADIN, which shows a slight increase in the median and a lower spread for future scenarios. The lag time (LT) is projected to decrease for all simulations. This consistent signal of a shorter period between rainfall centroid and flood peak indicates a projected increase in the flood flashiness in this basin (FI). In more detail, the median of the flashiness index, representing reactivity and intensity of floods, is projected to increase for all simulations. The smallest increase in the flashiness index is shown for GR5H-AROME, while the other simulations show noteworthy positive trends and higher spreads. Some outliers reach extremely high values in the future projections, warning of potentially rare, but very extreme, flood events occurring in the future. This result is in agreement with the study of Li et al. (2022b), who found an increase in flash flood potential over the USA in the high-end emission scenario, notably in southern regions that have a wet convective season such as this Mediterranean catchment. However, the projected changes over this catchment should be interpreted with caution as they come from a single pair of RCM and CPM simulations. With these preliminary results, the different impacts of climate change on flood characteristics highlight the fine-scale benefits of CPM in simulating underlying hydrological processes. The robustness of the climate change impact on flash floods still needs to be confirmed with a comprehensive study that includes an uncertainty assessment.

## 5 Conclusions

Given the devastating consequences of Mediterranean floods, it is necessary to project whether the recent increase in precipitation extremes is going to continue in a warmer climate and have impacts on flood severity. Until now, flood projections have been based on regional climate models that cannot accurately simulate precipitation extremes that are yet the main factor causing these floods. In the last 10 years, emerging regional convection-permitting climate models using resolutions of a few kilometers show encouraging results in the simulations of short-duration precipitation extremes, opening the door to enhanced confidence and realism in future flood projections.

This study compared two regional climate model simulations used as inputs of two hydrological models to evaluate the possible climate change impacts on floods in a Mediterranean basin. The AROME convection-permitting climate model (CPM) with a 2.5 km spatial resolution has been compared to the ALADIN model with a 12 km spatial resolution. The evaluation of climate simulations shows similar results for both models regarding the reproduction of the annual cycles of temperature and precipitation. There is no added value of using the CPM for the representation of the seasonality of temperature and precipitation. Historical climate simulations are globally too cold with a wet bias for spring and summer. ALADIN and AROME both projected hotter and drier summers in the future but no drastic changes in the wet season duration and intensity, except a weak increase for ALADIN for the wet season precipitation peak. The added value of the CPM can be clearly seen on rainfall simulation before bias correction, with a much better representation of extremes with AROME compared to ALADIN, with the latter showing a strong underestimation. However, the bias correction reduces the difference between models since they are both corrected to match the observations. Both models project an increase in hourly precipitation under the RCP8.5 scenario. That signal is stronger for events above the 99.9th percentile than more moderate events above the 95th percentile, with a stronger signal for AROME.

Yet, both climate simulations required a bias correction to reproduce the observed discharge and notably flood events. Similar simulations of flood events have been obtained with the two hydrological models considered, a lumped conceptual model, GR5H, and a spatially distributed, process-based model, CREST, with slightly better results with the GR5H model. Very similar future projections have been obtained with the two hydrological models, highlighting the robustness of the results given the two different types of model structures. In terms of floods, the hydrological simulations driven by the climate model outputs showed contrasted discharge projections, with a general increase in the flood hazard using the ALADIN RCM and, on the contrary, an increase only for the largest floods using the AROME CPM. This indicates that the type of climate model can strongly
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Box plots of historical (light orange) and RCP8.5 future (dark orange) flood event characteristics (b) and related rainfall events (a) using the bias-corrected simulations. Each characteristic is detailed in Sect. 3.3.</figcaption></figure>

modulate how the increase in extreme rainfall could be trans-
lated into changes in flood hazards. The AROME projections
are more in line with previous studies indicating no changes,
or a decline of small to moderate floods, caused by a damp-
ening effect due to depleted soil moisture and base flow be-
fore floods, while the most extreme floods are likely to in-
crease along with the more extreme future rainfall. All sim-
ulations also suggest an increase in the flashiness behavior
of the basin, with decreased lag times between rainfall and

runoff and a larger direct runoff contribution to floods, that
could make the flood warning and flood mitigation strategies
more difficult in this basin and beyond.

The results of the present study have been obtained with
a rather complex, but standard, modeling chain, linking cli-
mate models, bias correction and hydrological models. While
there are inherent uncertainties in the different steps of the
methodology applied herein, the relevance of the results is
reinforced since the two hydrological models provided sim-
<figure><img src="image_12.png" /><figcaption>**Figure 8.** Box plots of historical (light orange) and RCP8.5 future (dark orange) flood event characteristics (b) and related rainfall events (a) using the bias-corrected simulations. Each characteristic is detailed in Sect. 3.3.</figcaption></figure>

modulate how the increase in extreme rainfall could be trans-
lated into changes in flood hazards. The AROME projections
are more in line with previous studies indicating no changes,
or a decline of small to moderate floods, caused by a damp-
ening effect due to depleted soil moisture and base flow be-
fore floods, while the most extreme floods are likely to in-
crease along with the more extreme future rainfall. All sim-
ulations also suggest an increase in the flashiness behavior
of the basin, with decreased lag times between rainfall and

runoff and a larger direct runoff contribution to floods, that
could make the flood warning and flood mitigation strategies
more difficult in this basin and beyond.

The results of the present study have been obtained with
a rather complex, but standard, modeling chain, linking cli-
mate models, bias correction and hydrological models. While
there are inherent uncertainties in the different steps of the
methodology applied herein, the relevance of the results is
reinforced since the two hydrological models provided sim-
ilar conclusions using the same bias correction method, thus highlighting the differences stemming from the climate simulations. However, there is a clear need to strengthen the conclusions by using a larger ensemble of CPM that is becoming increasingly available for impact studies (Pichelli et al., 2021). Similarly, to reach regionally relevant conclusions and notably to derive adaptation strategies for future flood risks, there is also a need to analyze a broader ensemble of simulated floods on different catchments and to evaluate how their different areas and properties could modulate floods in a changing climate.

**Code availability.** The CREST/EF5 software is available at http://ef5.ou.edu/ (FLASH, 2024), the airGR package is available at https://doi.org/10.15454/EX11NA (Coron et al., 2020) and the SBCK R package is available at https://github.com/yrobink/SBCK (Robin, 2022).

**Data availability.** Hourly river discharge data could be accessed at https://hydro.eaufrance.fr/ (HydroPortail, 2024), and COMEPHORE radar rainfall is available on request from https://radarsmf.aeris-data.fr/ (AERIS, 2024). The AROME and ALADIN hourly simulations are available from the corresponding author upon reasonable request.

**Author contributions.** NP designed the climate and hydrological experiments and wrote the initial draft. PLP, YT and GT designed the experiments and revised the initial draft. JG and HV contributed to the setup of CREST and revised the manuscript. AA contributed to the data management and revised the manuscript.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** Thanks are given to the CNRM MOSCA team for their technical help, in particular to Erwan Brisson, Cecile Cailaud and Edith Cortez.

**Financial support.** This work is a contribution to the project KM-IMPACTS funded by the Make Our Planet Great Again initiative that is supported by the French National Research Agency under the future investment program ANR-18-MPGA-0005. This work was also supported by the EUCP project (European Union's

Horizon 2020 program, grant agreement no. 776613). This study has received funding from Agence Nationale de la Recherche – France 2030 as part of the PEPR TRACCS program under grant number ANR-22-EXTR-0011 (LOCALISING).

**Review statement.** This paper was edited by Maria-Carmen Llasat and reviewed by two anonymous referees.

## References

Addor, N. and Seibert, J.: Bias correction for hydrological impact studies – beyond the daily perspective: Invited Commentary, Hydrol. Process., 28, 4823–4828, https://doi.org/10.1002/hyp.10238, 2014.

AERIS: Home Page, https://radarsmf.aeris-data.fr/ (last access: 21 March 2024), 2024.

Alfieri, L., Smith, P. J., Thielen-del Pozo, J., and Beven, K. J.: A staggered approach to flash flood forecasting – case study in the Cévennes region, Adv. Geosci., 29, 13–20, https://doi.org/10.5194/adoe-29-13-2011, 2011.

Alfieri, L., Burek, P., Feyen, L., and Forzieri, G.: Global warming increases the frequency of river floods in Europe, Hydrol. Earth Syst. Sci., 19, 2247–2260, https://doi.org/10.5194/hess-19-2247-2015, 2015.

Amponsah, W., Ayral, P.-A., Boudevillain, B., Bouvier, C., Braud, I., Brunet, P., Delrieu, G., Didon-Lescot, J.-F., Gaume, E., Lebouc, L., Marchi, L., Marra, F., Morin, E., Nord, G., Payrastre, O., Zoccatelli, D., and Borga, M.: Integrated high-resolution dataset of high-intensity European and Mediterranean flash floods, Earth Syst. Sci. Data, 10, 1783–1794, https://doi.org/10.5194/essd-10-1783-2018, 2018.

Ascott, M. J., Christelis, V., Lapworth, D. J., Macdonald, D. M. J., Tindimugaya, C., Iragena, A., Finney, D., Fitzpatrick, R., Marsham, J. H., and Rowell, D. P.: On the application of rainfall projections from a convection-permitting climate model to lumped catchment models, J. Hydrol., 617, 129097, https://doi.org/10.1016/j.jhydrol.2023.129097, 2023.

Astagneau, P. C., Bourgin, F., Andréassian, V., and Perrin, C.: When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias, Hydrolog. Sci. J., 66, 1288–1305, https://doi.org/10.1080/02626667.2021.1923720, 2021.

Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belšić, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Christensen, O. B., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J. E., Hodnebrog, Ø., Kartosios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Remedio, A. R., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. M., Stocchi, P., Tölle, M. H., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K., Wulfmeyer, V., and Zander, M. J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w, 2021.
ilar conclusions using the same bias correction method, thus highlighting the differences stemming from the climate simulations. However, there is a clear need to strengthen the conclusions by using a larger ensemble of CPM that is becoming increasingly available for impact studies (Pichelli et al., 2021). Similarly, to reach regionally relevant conclusions and notably to derive adaptation strategies for future flood risks, there is also a need to analyze a broader ensemble of simulated floods on different catchments and to evaluate how their different areas and properties could modulate floods in a changing climate.

**Code availability.** The CREST/EF5 software is available at http://ef5.ou.edu/ (FLASH, 2024), the airGR package is available at https://doi.org/10.15454/EX11NA (Coron et al., 2020) and the SBCK R package is available at https://github.com/yrobink/SBCK (Robin, 2022).

**Data availability.** Hourly river discharge data could be accessed at https://hydro.eaufrance.fr/ (HydroPortail, 2024), and COMEPHORE radar rainfall is available on request from https://radarsmf.aeris-data.fr/ (AERIS, 2024). The AROME and ALADIN hourly simulations are available from the corresponding author upon reasonable request.

**Author contributions.** NP designed the climate and hydrological experiments and wrote the initial draft. PLP, YT and GT designed the experiments and revised the initial draft. JG and HV contributed to the setup of CREST and revised the manuscript. AA contributed to the data management and revised the manuscript.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** Thanks are given to the CNRM MOSCA team for their technical help, in particular to Erwan Brisson, Cecile Cailaud and Edith Cortez.

**Financial support.** This work is a contribution to the project KM-IMPACTS funded by the Make Our Planet Great Again initiative that is supported by the French National Research Agency under the future investment program ANR-18-MPGA-0005. This work was also supported by the EUCP project (European Union's

Horizon 2020 program, grant agreement no. 776613). This study has received funding from Agence Nationale de la Recherche – France 2030 as part of the PEPR TRACCS program under grant number ANR-22-EXTR-0011 (LOCALISING).

**Review statement.** This paper was edited by Maria-Carmen Llasat and reviewed by two anonymous referees.

## References

Addor, N. and Seibert, J.: Bias correction for hydrological impact studies – beyond the daily perspective: Invited Commentary, Hydrol. Process., 28, 4823–4828, https://doi.org/10.1002/hyp.10238, 2014.

AERIS: Home Page, https://radarsmf.aeris-data.fr/ (last access: 21 March 2024), 2024.

Alfieri, L., Smith, P. J., Thielen-del Pozo, J., and Beven, K. J.: A staggered approach to flash flood forecasting – case study in the Cévennes region, Adv. Geosci., 29, 13–20, https://doi.org/10.5194/ageo-29-13-2011, 2011.

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Vinet, F.: Geographical analysis of damage due to flash floods in southern France: The cases of 12–13 November 1999 and 8–9 September 2002, Appl. Geogr., 28, 323–336, https://doi.org/10.1016/j.apgeog.2008.02.007, 2008.

Vinet, F., Cherel, J.-P., Weiss, K., Lewandowski, M., and Boissier, L.: Flood related mortality in the French Mediterranean region (1980–2020), Hydrosci. J., 108, 2097022, https://doi.org/10.1080/27678490.2022.2097022, 2022.

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Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter, J. Geophys. Res.-Atmos., 121, 5237–5258, https://doi.org/10.1002/2015JD024511, 2016.

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Vautard, R., Kadygrov, N., Iles, C., Boberg, F., Buonomo, E., Bülow, K., Coppola, E., Corre, L., van Meijgaard, E., Nogherotto, R., Sandstad, M., Schwingshackl, C., Somot, S., Aalbers, E., Christensen, O. B., Ciarlo, J. M., Demory, M.-E., Giorgi, F., Jacob, D., Jones, R. G., Keuler, K., Kjellström, E., Lenderink, G., Levavasseur, G., Nikulin, G., Sillmann, J., Solidoro, C., Sørland, S. L., Steger, C., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Evaluation of the Large EURO-CORDEX Regional Climate Model Ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032344, https://doi.org/10.1029/2019JD032344, 2021.

Vinet, F.: Geographical analysis of damage due to flash floods in southern France: The cases of 12–13 November 1999 and 8–9 September 2002, Appl. Geogr., 28, 323–336, https://doi.org/10.1016/j.apgeog.2008.02.007, 2008.

Vinet, F., Cherel, J.-P., Weiss, K., Lewandowski, M., and Boissier, L.: Flood related mortality in the French Mediterranean region (1980–2020), Hydrosci. J., 108, 2097022, https://doi.org/10.1080/27678490.2022.2097022, 2022.

Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global climate model: description and basic evaluation, Clim. Dynam., 40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013.

Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., and Somot, S.: Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment, Nat. Hazards Earth Syst. Sci., 12, 2769–2784, https://doi.org/10.5194/nhess-12-2769-2012, 2012.

Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter, J. Geophys. Res.-Atmos., 121, 5237–5258, https://doi.org/10.1002/2015JD024511, 2016.

Vrugt, J. A., ter Braak, C. J., Diks, C. G., Robinson, B. A., Hyman, J. M., and Higdon, D.: Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling, Int. J. Nonlin. Sci. Numer. Simul., 10, 273–290, https://doi.org/10.1515/IJNSNS.2009.10.3.273, 2009.

Wang, J., Hong, Y., Li, L., Gourley, J. J., Khan, S. I., Yilmaz, K. K., Adler, R. F., Policelli, F. S., Habib, S., Irwn, D., Limaye, A. S., Korme, T., and Okello, L.: The coupled routing and excess storage (CREST) distributed hydrological model, Hydrolog. Sci. J., 56, 84–98, https://doi.org/10.1080/02626667.2010.543087, 2011.

Wasko, C. and Guo, D.: Understanding event runoff coefficient variability across Australia using the hydroEvents R package, Hydrol. Process., 36, e14563, https://doi.org/10.1002/hyp.14563, 2022.

Wasko, C., Guo, D., Ho, M., Nathan, R., and Vogel, E.: Diverging projections for flood and rainfall frequency curves, J. Hydrol., 620, 129403, https://doi.org/10.1016/j.jhydrol.2023.129403, 2023.

Zittis, G., Bruggeman, A., and Lelieveld, J.: Revisiting future extreme precipitation trends in the Mediterranean, Weather Clim. Extrem., 34, 100380, https://doi.org/10.1016/j.wace.2021.100380, 2021.