# Extreme dry spells over the Mediterranean Basin during the wet season: assessment of HyMeX/Med-CORDEX regional climate simulations (1979-2009).

Florian Raymond, Philippe Drobinski, Albin Ullmann, Pierre Camberlin

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## To cite this version:

Florian Raymond, Philippe Drobinski, Albin Ullmann, Pierre Camberlin. Extreme dry spells over the Mediterranean Basin during the wet season: assessment of HyMeX/Med-CORDEX regional climate simulations (1979-2009). International Journal of Climatology, 2018, 38 (7), pp.3090-3105. 10.1002/joc.5487. hal-01862692

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# Extreme dry spells over the Mediterranean Basin during the wet season: assessment of HyMeX/Med-CORDEX regional climate simulations (1979-2009)

Florian Raymond · Philippe Drobinski ·
Albin Ullmann · Pierre Camberlin

Received: date / Accepted: date

**Abstract** Exceptional dry spells, in this study referred to as very long dry spells (VLDS), are natural hazards to which the Mediterranean region is extremely vulnerable, with socio-economic and environmental impacts. In this study, they are characterized in terms of location, spatial extent, duration, temporal variability and associated atmospheric circulations. The main objective is to assess the performance of five HyMeX/Med-CORDEX regional climate simulations to detect and reproduce VLDS in comparison with the E-OBS observed daily gridded data. Models accurately reproduce the occurrence of precipitation around the Mediterranean Basin, and therefore the occurrence of VLDS, with at least 51% of the E-OBS VLDS reproduced by the regional simulations. They also accurately simulate the spatio-temporal characteristics. A second objective is to identify the synoptic atmospheric patterns associated

<footnote>F. Raymond
Centre de Recherches de Climatologie, Biogéosciences, Université de Bourgogne Franche-Comté, Dijon, France
Tel.: +33 380393822
E-mail: florian.raymond@u-bourgogne.fr</footnote>

<footnote>P. Drobinski
LMD/IPSL, École Polytechnique, Université Paris Saclay, ENS, PSL Research University, Sorbonne Universités, UPMC Univ Paris 06, CNRS, Palaiseau, France
Tel.: +33 169335142
E-mail: philippe.drobinski@lmd.polytechnique.fr</footnote>

<footnote>A. Ullmann
Centre de Recherches de Climatologie, Biogéosciences, Université de Bourgogne Franche-Comté, Dijon, France
Tel.: +33 380393822
E-mail: albin.ullmann@u-bourgogne.fr</footnote>

<footnote>P. Camberlin
Centre de Recherches de Climatologie, Biogéosciences, Université de Bourgogne Franche-Comté, Dijon, France
Tel.: +33 380393821
E-mail: pierre.camberlin@u-bourgogne.fr</footnote>
with VLDS. A clustering analysis shows that all models accurately reproduce the main VLDS spatial patterns, associated with anticyclonic conditions above the affected regions. The simulated VLDS occurrence is strongly related to the amplitude of the sea level pressure and 500 hPa geopotential height anomalies as well as the location of the maximum anomalies. All VLDS events are associated with anticyclonic conditions except those occurring in the Eastern Mediterranean, where they are generally not associated with a specific meteorological event but with the usual summer weather regime.

**Keywords** Extreme dry spells · Mediterranean Basin · RCM Models evaluation · HyMeX/Med-CORDEX

# 1 Introduction

The Mediterranean Basin is considered to be a major “hotspot” of climate change subject to strong warming and drying (IPCC, 2013), with increasing consequences on drought occurrence and severity. The Mediterranean climate is characterized by long, hot, dry summers, and mild winters during which rainfall occurs. Xoplaki et al. (2004) show that wet season precipitation accounts for between 50-60% (western and northern regions) and 70-90% (southern and eastern regions) of the total annual amount (see their Fig. 1d). A drought episode during the period generally dry (the summer) will not have the same impacts as a drought during the period generally wet (the winter). During the wet season, precipitations are hoped for example to raise the groundwater level, to maintain the soil moisture level and to raise the rivers level. Absence of rain on long periods during the wet season can immediately have strong environmental and societal impacts (on biodiversity and agriculture, for example).

Drought during the wet season (here characterized by the absence of rain over long periods) has impacts with, for instance, an increasing demand for irrigation to meet agricultural needs, and therefore increasing pressure on global water resources (Plan Bleu, 2014). Between 1961 and 2006, the percentage of cultivated areas equipped for irrigation increased from 17.1% to 22.7% in North Africa, and from 5.8% to 14.2% in southern Europe (FAO, 2011). In Spain, 20% of the water used for irrigation comes from groundwater (Hernandez-Mora et al., 2007), reaching 33% in North Africa and even 60% in the Maghreb countries (Siebert et al., 2010). The remaining water used for irrigation comes from surface water (i.e. rivers, lakes, ponds, reservoirs and wetlands) or non-conventional water resources (i.e. treated wastewater and desalinated water) (Siebert et al., 2010). In North Africa, the pressure on total renewable water resources due to irrigation is about 170% (i.e., the ratio between available renewable water resources and irrigation water withdrawal), which is expected to increase to 204% in 2050 (FAO, 2011).

The absence of precipitation in wet season during long periods lowers groundwater levels, possibly leading to restrictions on irrigation in the subsequent months. Likewise, long periods without precipitation during the wet
season deplete soil moisture and put rain-fed crops at risk. Rainfed systems are still very important in Mediterranean countries (64% of cultivated areas in Italy and 90% in Morocco) (Jacobsen et al., 2012), and most smallholder farmers are reliant on them. Soil moisture depletion also promotes the development of summer heat waves (Ferranti and Vierbo, 2006; Zampieri et al., 2009; Stéfanon et al., 2012a,b; Stéfanon et al., 2014a), which can increase crop withering through heat stress (Wang et al., 2008; Bauweraerts et al., 2013; Stéfanon et al., 2014b).

As mention Terrado et al. (2014), hydropower resources are also highly threatened during long periods without precipitation in the Mediterranean basin. During dryness, the level of reservoirs decreases, that lead a decline in hydroelectric power production.

In the context of climate change, it is very important to explore present and future evolution of the climate, including dry spells occurring during the wet season in the Mediterranean Basin, to anticipate adaptation measures for different sectors that require large water needs (e.g. agriculture). To characterize dry spells, Anagnostopoulou et al. (2003) count the number of consecutive dry days (CDD) for 20 Greek meteorological stations, as Lana et al. (2006) over the Iberian Peninsula (through 46 stations). Kostopoulou and Jones (2005) detect the maximal number of CDD for 84 meteorological stations spread over the North-western Mediterranean basin, as Oikonomou et al. (2010), using the CDD index for 56 meteorological stations over the same Mediterranean sector. In the literature, dry spells are therefore mostly defined as the number of consecutive unrainy days at punctual measure, through pure statistical approach, rather than physical/meteorological approach. Thereby, this way of characterizing dry spell limits the analysis of the processes controlling their location, spatial extent, duration and recurrence at the whole Mediterranean scale.

A majority of 21st century scenarios show a drying of the Mediterranean (Giorgi and Lionello, 2008; Dubrovsky et al., 2014; Hertig and Tramblay, 2017). This decrease in average precipitation is found with either atmosphere-ocean Global Climate Models (GCM) (Giorgi and Bi, 2005; Zappa et al., 2015) or atmosphere Regional Climate Models (RCM) (Gibelin and Déqué, 2003; Déqué et al., 2005; Gao et al., 2006; Ulbrich et al., 2006; Lelieveld et al., 2012; Paxian et al., 2015; Rajczak and Schär, 2017) and coupled atmosphere-ocean Regional Climate Models (Somot et al., 2008).

Global climate models (GCMs) cover the total surface of the Earth, using relatively coarse resolution (usually greater than 100 km grid side). Unfortunately, this low horizontal resolution induces a weak consideration of the relief, of the coasts, but also of the land use, resulting in a weak restitution of precipitations in the GCMs (Kumar et al., 2013; Sillmann et al., 2013; Mehran et al., 2014), limiting studies on extreme events, impact studies and adaptation strategies associated with climate change. Conversely, RCMs, which are numerical representations of the climate on a given region, use a smaller resolution which enables them to better assimilate the states of the surface (relief, coasts, land cover) and therefore to better simulate surface fields such
as rainfall. RCMs are therefore better adapted to study smaller scale events as extreme ones. Furthermore, this study is a first step to explore the future projections of extreme dry spells variability in the context of climate change in the Mediterranean Basin. This is why the RCMs are used in this study, rather than reanalysis or GCMs.

Studies already evaluate the regional models capacity to reproduce dry spells. For example, Herrera *et al.* (2010) use 9 RCM models from the ENSEMBLES project to assess the ability of the simulations to reproduce dry spells in Spain. Dry spells are characterised as maximal annual number of CDD at grid points scale. They found that the models tend to detect smaller values of CDD than in the observations. Domínguez *et al.* (2013) use 5 RCMs from the Spanish project ESCENA to assess the ability of the simulations to reproduce yearly mean number of CDD at grid points scale. They found that the models tend to underestimate the CDD values over most of the domain. Lopez-Franca *et al.* (2015) also used 5 models from ESCENA project to detect dry spells in Spain. In their study, dry spells are characterised as number of CDD, averaged on an annual scale for each grid point scale (they use the "annual average of dry spells length" index, AADSL). They observe that the 5 regional climate model simulations well reproduce the observed AADSL pattern in Spain. Fantini *et al.* (2017) use 9 models from the Euro-CORDEX and Med-CORDEX experiments to assess the ability of the simulations to reproduce daily precipitation statistics, as frequency of dry days or dry spells (CDD95 method, 95th percentile of the CDD durations at grid points scale, to characterize dry spell length). They found that models tend to underestimate the CDD95 over wetter regions of the Mediterranean basin. Unfortunately, none of these studies has clearly defined the notion of dry spells as climatic objects, based on their spatiotemporal characteristics (location, extent, and recurrence) at the Mediterranean scale.

Conducted in the frame of the HyMeX program (Hydrological cycle in the Mediterranean Experiment) (Drobinski *et al.*, 2014) and Med-CORDEX programs (Ruti *et al.*, 2015), this study, builds on the dry spell detection methodology by Raymond *et al.* (2016b), assess how regional climate simulations already performed reproduce the spatio-temporal characteristics of the very long dry spells (VLDS). This multi-model intercomparison analysis, highlighting the models resemblances and differences in VLDS detection, also discuss about the uncertainty of the results. The quantification and analysis of uncertainty is an objective in itself especially as a prerequisite for climate change studies.

Section 2 details the dataset (observations and simulations) and analysis methodology. Section 3 compares simulated with observed VLDS events in terms of duration, spatial extent, location and seasonality. Section 3.5 analyses the capacity of the simulations to reproduce the main persistent VLDS spatial patterns in the Mediterranean Basin, as well as the associated synoptic weather conditions. Section 4 and section 5 discuss some points and conclude the study.
## 2 Data and methodology

This study covers the 1979-2009 period, represented by 30 wet seasons (from September to April, 242 days per wet season), for a total of 7260 studied days.

### 2.1 E-OBS gridded data set

In this study, the E-OBS precipitation gridded dataset v.10.0 (Haylock et al., 2008) is used as a reference observation. This daily time-step gridded dataset, at 0.25° resolution and available from 1950 to 2013, is obtained by interpolation of observed precipitation time series (more 7 300 stations) over Europe, the Middle-East and North Africa, provided by the European Climate Assessment & Dataset project (ECA&D; Klok and Klein Tank, 2009). In this study, the E-OBS dataset are used over the 1979-2009 period (30 wet seasons). E-OBS was largely used for regional climate model evaluation (e.g. Lorenz and Jacob, 2010; Kjellström et al., 2011; Nikulin et al., 2011; Samuelsson et al., 2011; Torma et al., 2011; Flaounas et al., 2013; Stéfanon et al., 2014a; Vaittinada Ayar et al., 2016; Drobinski et al., 2016).

As mentioned by Hoftra et al. (2009), such a dataset contains inevitably some errors and uncertainties, which come from many sources (e.g. changes in station locations, measurement errors, interpolation uncertainties). This is why the E-OBS dataset has been comprehensively evaluated in the literature (Hofstra et al., 2009, 2010; Kyselý and Plavcová, 2010; Herrera et al., 2012; Flaounas et al., 2012). These evaluations show that E-OBS dataset is an interesting database, but should be used appropriately. For example, these studies warn against the validity of the dataset on low-density weather station areas. For example, Herrera et al. (2012) compared Spain02 and E-OBS in Spain and found that results are affected by the station density. Similarly, Flaounas et al. (2012) validated the E-OBS dataset in the Mediterranean Basin and conclude that it must be used with care in complex environments such as coastal areas and mountainous regions. These studies point out that the E-OBS dataset is not suited to the study of extreme weather events such as heat waves or intense precipitations, because temperature and extreme precipitation values are strongly undervalued compared to the station observations. However, the use of E-OBS is appropriate in this study, because the VLDS refer to the frequency of the precipitation and not to the daily cumulative precipitation. These uncertainties in the E-OBS dataset have forced us to removed several grid points from the series (mainly located in North Africa and Middle-East) because too few meteorological stations were used for interpolation and many complete years was missing from the time series of these grid points (Brunet et al., 2014). The choice was made to remove all grid points showing at least one complete wet season without data on the studied period (2 358 grid points removed). Maps showing the meteorological stations used for the first E-OBS precipitation interpolation are available in Haylock et al. (2008) or in Klok and Klein Tank (2009).
## 2.2 HyMeX/MED-CORDEX simulations

Five hindcast simulations are analysed over the 1979 and 2009 period (Table 1). The regional climate simulations, named after the model name and version, were produced in the frame of the HyMeX program (Drobinski et al., 2014) and the Med-CORDEX initiative (Ruti et al., 2015), which is the multi-model comparison exercise in the Mediterranean conducted in the frame of the Coordinated Downscaling Experiment (CORDEX) of the World Climate Research Program (WCRP) (Giorgi et al., 2009). The simulation dataset can be downloaded at www.medcordex.eu/medcordex.php or www.hymex.org. All regional climate simulations were driven at their lateral boundaries by the 6-hourly ERA-Interim reanalysis. Among the five climate models, three are regional limited-area models and use wind components, humidity, temperature or potential temperature and geopotential height as boundary conditions (CCLM4 - Rockel et al., 2008; Reg-CM4 - Giorgi et al., 2012; ALADIN52 - Colin et al., 2010; Hermann et al., 2011). The LMDZ4 climate model (Hourdin et al., 2006), and its version coupled with the ocean model NEMOMED8 (Beuvier et al., 2010) (hereafter referred as LMDZ4-NEMOMED8), is a global model with regional zoom capacity. Temperature, wind speed and specific humidity are nudged towards ERA-Interim outside the Med-CORDEX domain. All simulations but LMDZ4-NEMOMED8 use the ERA-Interim sea surface temperature as lower boundary condition over the sea. The five simulations cover different periods (Table 1). The common period of all simulations is 1979-2009 and is the period over which the model assessment, model inter-comparison and data analysis are performed in the following. The five simulations have a resolution of 0.44° (about 50 km). To facilitate the assessment and inter-comparison, the simulation outputs at 0.44° resolution are resampled onto the E-OBS grid at 0.25° resolution, using the nearest neighbors method (the Med-CORDEX grid points are reassigned to the nearest E-OBS grid points). The decision about the number of models selected for our study still prone to subjectivity and is somewhat arbitrary decision, as in the most of studies. These 5 Med-CORDEX models are retained in this study, because they all have the same resolution, and their historical and RCP trajectories runs are available, that will be used in a further work to explore the future VLDS variability.

## 2.3 Very long dry spells: detection and classification

This study investigates the Med-cordex models capacity to reproduce very long dry spell (VLDS) variability in the Mediterranean Basin during the wet season, compared to E-OBS. The methodology used, that is the same as thoroughly described in Raymond et al. (2016b) and inspired by Stéfanon et al., (2012a), is briefly presented hereafter.

As Mediterranean rainfall occurs mainly in autumn, winter, and early spring, this study focuses on the period from September to April (“wet season” hereafter), over an area 10°W-40°E and 28°N-46°N (“study area” hereafter),
from 1979 to 2009 (30 wet seasons of 242 days). We decide to start in September for two reasons. Firstly because it is the beginning of the wet period in the large part of the Mediterranean basin. Secondly, even if the raining season has not yet set in September in the Eastern part of the Mediterranean basin, the length of the seasonal VLDS detected during this month has a significant impact on the onset of the wet season in this sub-region (see after). Dry spells are defined as consecutive days with less than 1 mm of precipitation, and are firstly classified as follows: Very Short Dry Spells (VSDS), shorter than the 20th percentile of dry spell duration; Short Dry Spells (SDS), between the 20th and 40th percentile; Medium Dry Spells (MDS), between the 40th and 60th percentile; Long Dry Spells (LDS) between the 60th and 80th percentile; Very Long Dry Spells (VLDS), longer than the 80th percentile of dry spell duration (cf. section 3.1). Apply the different centiles only on the dry spell durations (and not on all the dry spells detected) permit to create a dry spell index with the same amplitude in the five classes. Percentiles of dry spell durations is computed at each grid points over the 1979-2009 period, for the E-OBS dataset and for the five regional climate simulations. In this study, we focus on the very long dry spell events only (the VLDS), because they are dry spells with the longest duration and associated with the strongest impacts for the society and environment. The value "1" is then affected to the grid points where and when VLDS occurs and "0" to all other grid points (rainy days, dry spells shorter than the 80th percentile, or grid points above the Mediterranean Sea).

Like heatwaves, droughts are not local phenomena. The identification method therefore assumes a certain amount of spatial coherence. Following the work of Stéfanon et al., (2012a) on the identification of heatwaves, a sliding scan is performed over the entire study area, with a square window of 6° in longitude and latitude (0.5° sliding span in our study). For a given square, the date and grid points are counted as part of a VLDS event if 50% of the grid points are flagged "1"; only those grid points flagged "1" are retained for VLDS event characterization. This eliminates isolated grid points and structures too limited in spatial extent. The sensitivity of this method, to the size of the square, and to the grid point percentage threshold, was tested. Squares of less than 6° retain some local events without spatial coherence, and squares of more than 6° retain only a few events, with patterns covering nearly half of the study area. Using a smaller (greater) percentage has the same effect as using smaller (larger) windows. Majority of the VLDS detected are "simple", because only one coherent sector of the entire study area is affected by the event. Conversely, some of the VLDS detected are "complex", because they affect two or three separate sectors within the study area, at the same time, but for different durations. For complex events, sectors are considered separate when, between them, there is at least one sliding square window for which no VLDS event is detected.

A clustering approach (agglomerative hierarchical clustering, AHC) is used to identify persistent and recurrent spatial patterns of VLDS in the Mediterranean Basin (Gong and Richman, 1995). In order to classify the VLDS events,
it is necessary to select the most representative day for each VLDS event. Applying AHC from all the days affected by VLDS would bias the results, because the longer events would be over-represented compared to the shorter ones. As Raymond *et al.* (2016b) showed, the central day is the date that generally best characterizes the maximum spatial extension of each events. The central day is the date exactly in the middle of the period affected by the event. The AHC algorithm (using the Euclidean distance metric) is then applied to the binary matrix, but only for the central days of the VLDS events detected in the E-OBS dataset.

In order to study the capacity of the models to reproduce the main VLDS patterns detected in E-OBS, we retain the same number of VLDS patterns for the 5 climatic models as for E-OBS, although this division does not systematically show the lowest intraclass variability in the models clustering approach. The k-nearest neighbors algorithm (k-NN) is used to re-associate the patterns detected from the models to the patterns detected from E-OBS. This method is based on the euclidean distance, to gather the spatial configurations according to their similarity with respect to a reference configuration (Zouhal and Denoeux, 1997).

In this study, the similarities with the Raymond *et al.* (2016b) study are mainly based on the methodology used. The results obtained in this study with the E-OBS dataset (number of VLDS and VLDS patterns obtained) are slightly different from those obtained in Raymond *et al.* (2016b), because the percentile 80 are calculated from dry spells detected on two slightly different periods (1979-2013 in Raymond *et al.* (2016b); 1979-2009 in this study), and the periods used to detect VLDS are different (1957-2013 on Raymond *et al.* (2016b); 1979-2009 on this study). Finally, it is not exactly the same VLDS detected and the same VLDS patterns between the 2 studies, even if the methodology and the reference observation dataset are the same.

## 3 VLDS characteristics

### 3.1 The dry spells 80th percentile values

Figure 1 shows the value of the dry spells 80th percentile duration at each grid point of the common grid between the E-OBS dataset (Fig.1a) and the regional climate simulations biases compare to E-OBS (Figs.1b,c,d,e,f), for the 1979-2009 period. The 80th percentiles values are the starting point of the VLDS detection. The pattern of the dry spells 80th percentile duration estimated from the E-OBS dataset, is very similar to the precipitation occurrence pattern shown in Figure 4 of Raymond *et al.* (2016a). 80th percentile are longer in regions where it rains less frequently (Iberian Peninsula except north, Maghreb - i.e. Morocco and Algeria - and Levant). Conversely, 80th percentile are shorter in regions where it rains more frequently (northern Iberian Peninsula, France, Italy, west coast of the Balkans). Orography or coastal effects are clearing playing a role on the structures seen in the Figure 1a, especially for Maghreb,
Iberian Peninsula, which are protected to the Western wind, which explains less frequent precipitations and higher values of p80 in these regions. Balkans are also protected to the Western wind, but the seasonality of the monthly precipitations is less marked as for Maghreb or Iberian Peninsula. This explain why the values of the p80 in the Balkans are smoother than for the west side of the Mediterranean basin. The Levant region shows strong abrupt structures between the northern and the southern part. As Saaroni *et al.* shown (in their Figure 1), there is a marked difference between the northern and the southern part of the Levant, in terms of cumulative annual precipitations. As they shown, above 31°N of latitude, the cumulated annual precipitations are between 100 mm and 1400 mm. Below 31°N of latitude, it rains rarely, with cumulated annual precipitations lesser than 100 mm. This mainly explain the high p80 values on the southern part of the Levant.

All models, but CCLM4, underestimate 80^th^ percentile duration by about -10 days over much of the Mediterranean region. An exception is the Levant, where three models overestimate 80^th^ percentile duration in quasi the entire region (CCLM4, LMDZ4 and LMDZ4-NEMOMED8). The 80^th^ percentile duration in extreme south of the Levant is overestimated by the five models. ALADIN52 model slightly overestimates the 80^th^ percentile duration by about 10 days with respect to E-OBS (Fig.1d) which is much better than the other models which overestimate the 80^th^ percentile duration by more than 90 days.

In term of 80^th^ percentile duration for the coupled/uncoupled simulations of the LMDZ4 and LMDZ4-NEMOMED8 models, the results are comparable between the two simulations.

## 3.2 Duration and extension

The sliding scan detects a number of VLDS in all simulations which is reported in Table 2 for the 1979-2009 period. For the E-OBS dataset, the clustering method identifies 45 VLDS for a total duration of 2 602 days (36% of the total studied period). These 45 VLDS are spread over 27 of the 30 studied wet seasons (from 1 to 3 VLDS per season), 3 wet seasons are not affected by VLDS. The simulations reproduce a number of VLDS close to E-OBS: CCLM4 detects 47 events (42% of the total studied period, from 1 to 4 VLDS per season, spread over 27 of the 30 studied wet seasons), Reg_CM4 detects 38 events (27%, from 1 to 2 VLDS per season, spread over 27 of the 30 studied wet seasons), ALADIN52 detects 41 events (28%, from 1 to 3 VLDS per season, spread over 27 of the 30 studied wet seasons), LMDZ4 detects 46 events (39%, from 1 to 3 VLDS per season, spread over 27 of the 30 studied wet seasons) and LMDZ4-NEMOED8 detects 48 events (40%, from 1 to 3 VLDS per season, spread over 29 of the 30 studied wet seasons) (Table 2).

All simulations reproduced between 51% and 73% of the 45 VLDS detected in the E-OBS dataset (Table 2). CCLM4 reproduces 32 VLDS detected in E-OBS (71% of the VLDS detected in E-OBS), Reg_CM4 reproduces 23 VLDS detected in E-OBS (51%, the lowest percentage), ALADIN52 reproduces 26
VLDS detected in E-OBS (58%), LMDZ4 reproduces 31 VLDS detected in E-OBS (69%) and LMDZ4-NEMOED8 reproduces 33 VLDS detected in E-OBS (73%, the highest percentage). Two step allow us to consider that one model reproduces a VLDS detected in E-OBS: when at least 50% of the days affected by an VLDS in the E-OBS dataset are also simulated as days impacted by an VLDS in the model (temporality parameter), and then, when at least 20% of the grid points affected by an VLDS in the E-OBS dataset are also affected by an VLDS in the model, during the common VLDS days (location parameter). The 20% threshold of grid points is used because models can reproduce VLDS observed, but with smaller/larger spatial extent, and/or slightly shifted compared to the E-OBS dataset event detected.

Finally, according to this two conditions, CCLM4 shows 96% of common days and 61% of common grid points with the 32 VLDS it reproduces from E-OBS (in average), Reg_CLM4 shows 90% of common days and 49% of common grid points with the 23 VLDS it reproduces from E-OBS (in average), ALADIN52 shows 86% of common days and 53% of common grid points with the 26 VLDS it reproduces from E-OBS (in average), LMDZ4 shows 93% of common days and 54% of common grid points with the 31 VLDS it reproduces from E-OBS (in average) and LMDZ4-NEMOMED8 shows 92% of common days and 55% of common grid points with the 33 VLDS it reproduces from E-OBS (in average).

In terms of number of VLDS detected, the coupled version of the LMDZ4 model with the NEMOMED8 model detects 2 more VLDS events than the uncoupled version of LMDZ4 model. The coupled version also slightly better reproduces the observed VLDS (2 more observed VLDS reproduced by LMDZ4-NEMOMED8 model, compared to LMDZ4 model). The results are however close between these two versions of the LMDZ4 models.

Figure 2 shows two box plots for VLDS duration and spatial extent, respectively, estimated from the E-OBS dataset and the 5 simulations. Figure 2a shows that the variance of VLDS duration is smaller in E-OBS than in the regional climate simulations. A closer look shows that only the VLDS duration estimated from ALADIN52 simulation is significantly different from that estimated from E-OBS at 0.95 confidence level (Student T-test). All simulations, except CCLM4, underestimate the median duration value with respect to E-OBS median (between 50 and 56 days versus 57 days for E-OBS). The large overestimation of the VLDS duration in the Levant region makes the mean duration larger than the median value, except for CCLM4. Figure 2b shows that the simulated variance of VLDS spatial extent is smaller than the observed variance, except for CCLM4. Regarding the mean spatial extent, no simulations show result significantly different from E-OBS. The results are comparable between the coupled/uncoupled LMDZ4 simulations in terms of VLDS duration and spatial extent.
3.3 Affected regions

The Mediterranean Basin is now split into 7 sub-regions, mostly based on Mediterranean climatic sub-regions shown in the literature: the Maghreb (Born et al., 2008; Tramblay et al., 2013), the Iberian Peninsula (Lana et al., 2006; Vicente-Serrano, 2006; Sanchez et al., 2011), France (Vidal et al., 2010), Italy (Brunetti et al., 2002), the Balkans (Koleva and Alexandrov, 2008; Croitoru et al., 2012), the Anatolia region (Komuscu, 2001; Karabörk, 2007) and the Levant region (Törnros, 2010; Saaroni et al., 2015). Figure 3 shows the 7 Mediterranean sub-regions.

Table 3 shows VLDS duration and spatial extent for the 7 Mediterranean sub-regions, for E-OBS and the 5 simulations. It shows the differences between the 7 Mediterranean sub-regions in the five simulations and in the E-OBS dataset. All simulations accurately reproduce the main VLDS characteristics in the 7 sub-regions, in terms of mean duration and spatial extent. For E-OBS, the sub-regions with the longest VLDS are the Maghreb, the Iberian Peninsula, the Anatolia and the Levant regions. The VLDS in these sub-regions are also the longest in the simulations. Conversely, France, Italy and the Balkans display the shortest VLDS, in E-OBS but also in all simulations. Regarding the spatial extent, the Iberian Peninsula, the Balkans and the Levant regions display the largest VLDS, with more than 200 000 km² per event in E-OBS, but also in all simulations, except in the Levant region for Reg_CM4 and ALADIN52.

The majority of the differences between E-OBS and the simulations are not significant (according to the T-test at 0.95 confidence level). However, some sub-regions show significant differences between E-OBS and the simulations. Reg_CM4 significantly underestimates the mean VLDS duration in Italy and the Balkans with respect to E-OBS, whereas CCLM4 significantly overestimates the mean VLDS duration in Anatolia (Table 3). In the Levant sub-region, CCLM4, LMDZ4 and LMDZ4-NEMOMED8 significantly overestimate the mean duration of the VLDS compared to E-OBS while ALADIN52 significantly underestimate the mean VLDS duration in this region.

Almost all simulations underestimate the mean VLDS spatial extent in Maghreb, Iberian Peninsula, France, Italy and Balkans, but not significantly (according to the T-test at 0.95 confidence level). However, CCLM4 significantly overestimates the mean VLDS spatial extent in Anatolia compared to E-OBS whereas Reg_CM4 and ALADIN52 display significant underestimations in the Levant region compared to E-OBS. The results are comparable between the coupled/uncoupled LMDZ4 simulations in terms of mean VLDS duration and mean VLDS spatial extent, at a Mediterranean sub-regions scale. Table 3 confirms that models tend to detect more localised VLDS events, rather than shorter VLDS events, with respect to E-OBS.

Figure 4 is a synthesis of all previous results, it shows the sectors which are the most affected by VLDS, and the simulated biases for regional simulations. It displays the fraction of VLDS days at each grid point with regards to the 7 260 studied days. In E-OBS, the most affected regions are the south-west of the Med-CORDEX domain (southern Iberian Peninsula and north-western
Maghreb, between 4% and 10% of studied days) and south-east (Levant region, between 6% and 14% of studied days; Fig. 4a).

Reg_CM4 and ALADIN52 underestimate the fraction of VLDS days in almost the entire Mediterranean Basin and strongly in the Levant region (Figs 4c and 4d). LMDZ4 and LMDZ4-NEMOED8 also underestimate the fraction of the VLDS days in a large part of the Mediterranean Basin (Figs 4e and 4f). However, these two models overestimate the fraction of the VLDS days in the southern Anatolia and on almost the entire Levant, and strongly in the south-ern Levant. There is no differences between coupled/uncoupled version of the LMDZ4 model, in term of fraction of VLDS days. Conversely to the Reg_CM4, ALADIN52, LMDZ4 and LMDZ4-NEMOMED models, CCLM4 rather overes-timate the fraction of VLDS days in the Mediterranean Basin, except in central Iberian Peninsula, in France, in northwestern Italy and southern Levant (Fig. 4b).

The underestimation (overestimation) of the fraction of VLDS days, ob-served in the figure 4, may has 3 origins: (1) the number of VLDS detected in the simulations is too small/too large, (2) the VLDS duration is too short/too long, (3) the VLDS are too local/too wide, with respect to E-OBS. The global tendency of the Figure 4 (a quasi-generalized underestimation of the fraction of VLDS days) means that the models would tend to detect shorter and/or more localised VLDS events than E-OBS. For example, Reg_CM4 and ALADIN52 strongly underestimate the fraction of VLDS days in the Levant region because they both underestimate the 80th percentile duration (except to the extreme south; Fig.1), that directly influence the duration of the VLDS events. These two models also underestimate the number of VLDS events (Table 2), in par-ticular those affecting Levant (not shown), and significantly underestimate the mean duration and the spatial extent of the VLDS events in the Levant (Table 3).

## 3.4 VLDS seasonality

Figure 5 illustrates the VLDS seasonality in the E-OBS dataset and in the regional climate simulations, in the 7 Mediterranean sub-regions over the 1979-2009 period. The histograms show the month of the VLDS onset. One VLDS event may overlap several sub-regions, this is why, for observation and simulations, the sum of sub-regional VLDS events analysed in Figure 5 is higher than the total number of VLDS detected. VLDS starting in a given month can also overlap the following month.

The VLDS events occurring in the Eastern Mediterranean Basin (the Anatolia and Levant regions) clearly show evidence of VLDS occurrence seasonality. The majority of VLDS in these regions begin on September 1st, because they are the continuation of the seasonally dry summer period (called seasonal VLDS). In Eastern Mediterranean, there is no precipitation during September and most of October (Saaroni et al., 2015; Raymond et al., 2016b). In the present study, more than 80% of the observed VLDS in the Anatolia and
Levant regions begin in September (Figs.5f,g). As said before, the length of the seasonal VLDS detected during September has a impact on the onset of the wet season in this sub-region. Here, the ratio of 10% of the grid points synchronously affected by precipitations is used to apprehend the beginning of the wet season in the Levant. For E-OBS and for the seasons without VLDS in the Levant, the first day where at least 10% of the grid points are affected by precipitations in the Levant is the 6th of October (in average). Conversely, for the seasons with VLDS in the Levant, the first day where at least 10% of the grid points are affected by precipitations in the Levant is the 25th of October (in average). Simulated VLDS beginning September 1st in Eastern Mediterranean also reproduce the strong seasonality. They amount from 60% (ALADIN52) to more than 90% (CCLM4) affecting Anatolia, and from 70% (ALADIN52) to 100% (CCLM4 and Reg_CM4) affecting the Levant regions.

All simulations slightly overestimate the number of VLDS events starting in September in the Balkans (Fig.5e). For Maghreb and the Iberian Peninsula, observed VLDS occur with a weak seasonality over the entire wet season with 2 peaks at the beginning and in the middle of the wet season (Figs.5a,b,c). All simulations reproduce these 2 peaks, except in France where ALADIN52 is the only simulation capturing the mid-season peak of occurrence. There are too few observed VLDS in Italy to observe a significant seasonality (Fig.5d). The VLDS seasonality are comparable between the coupled/uncoupled LMDZ4 simulations.

## 3.5 VLDS spatial patterns: clustering analysis

To identify persistent and recurrent typical spatial patterns of VLDS in the Mediterranean, for the 1979-2009 period, a clustering approach based on a hierarchical ascendant classification is first used to classify the 45 VLDS events detected in E-OBS at the center date of the event. The number of clusters displaying the weakest intra-class variability is 5. An additional sensitivity analysis with 6, 7, 8 and 9 clusters has shown overlap in the geographical patterns of two or more clusters. Therefore, 5 clusters are kept based on both quantitative arguments and additional sensitivity analysis of the classified patterns. In a first step, we perform classification on each of the 5 models simulations to obtain 5 VLDS clusters (the same number as in E-OBS classification). In a second step, for each models, the k-NN algorithm is used to re-associate the patterns detected from the models to the patterns detected from the E-OBS.

Figure 6 displays the 5 clusters of the observed VLDS patterns and shows for each cluster the fraction of VLDS events: the first cluster, called “Generalized”, groups 2 VLDS events affecting a large part of the Mediterranean Basin; the second pattern, called “North-East”, groups 12 VLDS events mainly located in the Balkans and north-western Anatolia; the third pattern, called “Scattered”, groups 15 VLDS events, located mainly in the Western Mediterranean; the fourth pattern, called “South-East”, groups 12 VLDS events mainly located in the Levant region; the last pattern, called “West”, groups 4 VLDS
events mainly located in the Iberian Peninsula and Maghreb. Figure 6 highlights the most affected regions, for each clusters. It also shows the various VLDS patterns from the simulations. In general, all models reproduce fairly accurately the VLDS patterns classified using E-OBS data.

In details, notable differences can be found in a few simulations. ALADIN52 does not properly reproduce the North-East pattern. It groups 26 events distributed over the whole Mediterranean Basin. LMDZ4 and LMDZ4-NEMOMED8 fail in reproducing the Generalized pattern: the uncoupled version grouped 6 VLDS events centred in Anatolia, the coupled version grouped 2 VLDS events jointly localised in the western and eastern Mediterranean basin. For the four others VLDS patterns, the coupled/uncoupled versions of the LMDZ4 model show comparable results. However, LMDZ4 model groups 10 VLDS events in the South-East pattern, while LMDZ4-NEMOMED8 model groups 15 VLDS events.

As shown by Raymond *et al.* (2017), anticyclonic conditions are associated with the Mediterranean VLDS pattern, as also confirmed by Tramblay and Hertig (2018).

## 4 Discussion

As shown in the article, all the models (but ALADIN52) strongly overestimate the percentile duration by more than 90 days in the southern Levant. Such peculiar behaviour needs further analysis. Hofstra *et al.* (2009) show no particular problems with the E-OBS precipitation data in the southern Levant region. The problem in the southern Levant may be due to the models. Indeed, Vaittinada Ayar *et al.* (2016) compare with E-OBS data, daily precipitation simulated with 5 regional climate models (2 from Euro-CORDEX and 3 from HyMeX/Med-CORDEX) during winter (mid-October to mid-April) and over the 1989-2008 period. They show that the fraction of dry days in winter, at the Mediterranean Basin scale, is generally underestimated with respect to E-OBS. Vaittinada Ayar *et al.* (2016) suggest that regional climate models generally produce rainfall too frequently, that it is well-known and widely commented in the literature. Fantini *et al.* (2016) also indicate that Euro-CORDEX/Med-CORDEX models show a general tendency to underestimate the dry day frequency compared to the observations. This explains why all models, but CCLM4, mostly underestimate the dry spells 80th percentile duration in the Mediterranean Basin. Conversely, they show that all models strongly overestimate the fraction of dry days in the southern Levant region. They attribute such peculiar behaviour to the relaxation zone at the domain boundaries, for the regional models. LMDZ4 and LMDZ4-NEMOMED8 runs also overestimate the dry spells 80th percentile duration in most of the Levant, because the winter climate is complex in these sub-regions (short wet seasons) and the GCM shows pain to reproduce this particular climate.

As mentioned in the study, E-OBS dataset contains inevitably some errors and uncertainties, that forced us to remove several grid points from the se-
ries (mainly located in North Africa and Middle-East). The lack of the E-OBS dataset on North Africa and a part of the Eastern Mediterranean basin is especially damaging since these Mediterranean sectors are strongly vulnerable to climate change. This is why the use of others precipitation dataset, such as data from the Global Precipitation Climatology Centre (GPCC) or Climatic Research Unit (CRU), could have been considered. Unfortunately, these two precipitation dataset are at a monthly time-step, which is not suitable for the study of dry spells. Daily precipitation dataset from the Global Precipitation Climatology Project (GPCP) could have been considered. This database is quite different since it merges observations with satellite data, and are available only from October 1996. Daily precipitation dataset from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) also could have been considered in our study. This database, with a 0.5° resolution, covered the total global land areas but only for the 1979-2005 period (Chen et al., 2008). However, it would have been interesting to use CPC database to compare the results against E-OBS in term of VLDS events detection, especially for the sectors poorly covered by meteorological stations (south and east on the Mediterranean basin), but in a dedicated work. There are very few datasets on daily observation rainfall that cover the Mediterranean Basin and that have a satisfactory temporal depth to evaluate regional climate simulations. This is why up to now the E-OBS daily dataset is a good compromise to study the observed precipitation fields over Mediterranean, from the middle of the 20th century (see Raymond et al., 2016b). However, getting access to an updated database with more stations at the European and Mediterranean scales would be desirable.

The definition of the wet season used during this study (September to April) is adapted to the vast majority of the Mediterranean Basin, because September is a month subject to comparable cumulative precipitation than the seven other months of the season in France, Italy, the Balkans, and to a lesser extent in the Iberian Peninsula, Maghreb and Anatolia. Only the Levant observes a particularly dry September, being in the continuity of summers characterized by a lack of rain on this sector of the Mediterranean Basin. Thus, as seen in the study, the large majority of the VLDS detected in the Levant are not characterized as abnormal climatic phenomena, but are defined as seasonal dry events. However, the seasonal VLDS in the Levant are still interesting, because they show a late onset of the rainy season on this sector. Nevertheless, they can not be compared to the VLDS detected in the rest of the Mediterranean Basin. Removing September (or even October) from the definition of the wet season would probably permit to apprehend the VLDS occurring during the wet season in the Levant region, but would not have been satisfactory in order to apprehend these events in the rest of the basin. One solution to avoid issue with dry spells being an extension of the summer season would be to discard events for which September 1 is the beginning of the event. This study shows the complexity to studying as a whole a large sector subject to different regional climate modalities, such as the Mediterranean Basin.
## 5 Conclusion

How accurate are hindcast simulations to reproduce extreme dry spells (hereafter called very long dry spells - VLDS) in terms of duration and spatial extent is the central question addressed in this study. The hindcasts simulations analysed in this work were performed in the frame of the HyMeX and MEDCORDEX programs with 5 different regional climate models. Their assessment is conducted by processing the E-OBS dataset to retrieve VLDS duration, seasonality, and spatial extent. The processing is similarly applied to the simulations and the simulated characteristics are compared to the observed ones. All models simulate at least 70% VLDS events in common with the observed events. They also reproduce accurately VLDS seasonality as well as the main VLDS spatial patterns. All models but one (ALADIN52) simulate a larger variability of VLDS duration. Only ALADIN52 simulates significantly shorter VLDS events. Regarding the mean VLDS spatial extent, no significant difference is found between the observations and the simulations. At a sub-regional scale, the largest inter-model spread is found in the Eastern Mediterranean. Reg_CM4 and ALADIN52 tend to underestimate VLDS in the Levant region, contrary to LMDZ4 and LMDZ4-NEMOMED8, that overestimate VLDS in this Mediterranean sub-region.

The analysis of the synoptic atmospheric conditions associated with VLDS events show that the simulated VLDS occurrence is related to the amplitude of the sea level pressure and 500 hPa geopotential height anomalies as well as the location of the anomalies maxima. All VLDS events are associated with anticyclonic conditions except those occurring in the Eastern Mediterranean, where they are generally not associated with a specific meteorological event but with the usual summer weather regime. Among the five simulations, two were performed with one single atmospheric model. One simulation had prescribed sea-surface temperature (LMDZ4), the other simulation was run interactively with an ocean model (LMDZ4-NEMOMED8). Coupling LMDZ4 model to the ocean model NEMOMED8 don't improve/reduce the performance of the simulation, in term of VLDS simulations, as it is shown throughout the article. Even if the results from the two simulations are overall similar, the slight differences between the two versions (number of VLDS, VLDS patterns) would need further investigation and a dedicated work in a multi-model approach using other pairs of simulations run in similar configuration.

The analysis of each simulation, as well as inter-experiment spread, also would enable us to observe that the physical parameterizations of the models (convection, boundary-layer, microphysics, radiation) could have a weight on seasonal VLDS patterns, and its basic intraseasonal characteristics during the wet season (number, average intensity and dry spell spatial extent, duration and frequency). However, an in-depth insight of the different sets of parameterizations need a dedicated work.

This study is a first step towards the analysis of extreme dry spells impacts. Indeed, by investigating VLDS occurring during the wet season, this study focuses on climate events which have severe consequences in the follow-
ing months (premature crop withering, increased pressure on water resources, enhanced fire risk). By identifying typical VLDS spatial patterns, by quantifying their spatial extent and duration, this study brings the foundation for socio-economic and environmental impacts analysis, which has not been performed in such way in the existing literature. Future work will also be dedicated to the evolution of VLDS characteristics in the context of climate change in the Mediterranean Basin.

**Acknowledgements** This work is a contribution to the HYdrological cycle in The Mediterranean EXperiment (HyMeX) program, through INSUMISTRALS support, and the Mediterranean region COordinated Regional climate Downscaling EXperiment (Med-CORDEX) program. The authors are grateful to Marc Stéfanon for providing his heatwave identification processing software, which has been adapted to the context of droughts. They acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). They acknowledge the HyMeX database teams (ESPRI/IPSL and SEDOO/Observatoire Midi-Pyrns) and the MED-CORDEX database team at ENEA for their help in accessing the data. It is also a contribution to the HyMeX regional hydroclimate project of the GEWEX program of the World Climate Research Program (WCRP) (GEWEX Hydroclimate Panel).

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<table><caption>**Table 1** List of regional climate simulations performed in the frame of the HyMeX and Med-CORDEX programs and used in this study. The list includes the institute name, the model name and version, the horizontal resolution, and indicates whether an ocean/atmosphere coupled version is used and the periods over which the simulations were performed. In the text, the simulations are named after the model name and version.</caption><thead><tr><th>Institute</th><th>Model name and version</th><th>Resolution</th><th>Ocean/atmosphere coupling</th><th>Period</th></tr></thead><tbody><tr><td>CMCC</td><td>CCLM4</td><td>0.44°</td><td>No</td><td>1979-2012</td></tr><tr><td>ICTP</td><td>Reg_CM4</td><td>0.44°</td><td>No</td><td>1979-2012</td></tr><tr><td>CNRM</td><td>ALADIN52</td><td>0.44°</td><td>No</td><td>1979-2011</td></tr><tr><td>LMD</td><td>LMDZ4</td><td>0.44°</td><td>No</td><td>1979-2009</td></tr><tr><td>LMD</td><td>LMDZ4-NEMOMED8</td><td>0.44°</td><td>Yes</td><td>1979-2009</td></tr></tbody></table>

<table><caption>**Table 2** Number of VLDS events (first row), number of VLDS days (second row) and number of VLDS events in common in E-OBS and in the 5 regional climate simulations. VLDS days are days that are affected by VLDS event. The corresponding fraction in % is also indicated.</caption><thead><tr><th></th><th>E-OBS</th><th>CCLM4</th><th>Reg_CM4</th><th>ALADIN52</th><th>LMDZ4</th><th>LMDZ4-NEMOMED8</th></tr></thead><tbody><tr><td>Number of VLDS</td><td><strong>45</strong></td><td>47</td><td>38</td><td>41</td><td>46</td><td>48</td></tr><tr><td>Total number of days affected by VLDS<br>(corresponding fraction in %)</td><td><strong>2602<br>(36%)</strong></td><td>3040<br>(42%)</td><td>1987<br>(27%)</td><td>2001<br>(28%)</td><td>2814<br>(39%)</td><td>2901<br>(40%)</td></tr><tr><td>Number of E-OBS events reproduced<br>by the regional simulations<br>(corresponding fraction in %)</td><td></td><td>32<br>(71%)</td><td>23<br>(51%)</td><td>26<br>(58%)</td><td>31<br>(69%)</td><td>33<br>(73%)</td></tr></tbody></table>

<table><caption>**Table 3** VLDS characteristics (mean duration and spatial extent) for the 7 the Mediterranean sub-regions shown in Fig.3: the values shown in italics are estimates from E-OBS; the other values shown are estimated values from the simulations within the uncertainty range (without stars), significantly lower or significantly larger (with stars) than the E-OBS values, respectively (according to the t-test at 0.95 confidence level).</caption><thead><tr><th></th><th></th><th>E-OBS</th><th>CCLM4</th><th>Reg_CM4</th><th>ALADIN52</th><th>LMDZ4</th><th>LMDZ4-NEMOMED8</th></tr></thead><tbody><tr><td rowspan="6">Mean VLDS duration (days)</td><td>Maghreb</td><td><strong>52</strong></td><td>59</td><td>55</td><td>48</td><td>49</td><td>54</td></tr><tr><td>Iberian Peninsula</td><td><strong>52</strong></td><td>57</td><td>53</td><td>44</td><td>47</td><td>51</td></tr><tr><td>France</td><td><strong>35</strong></td><td>42</td><td>31</td><td>37</td><td>38</td><td>37</td></tr><tr><td>Italy</td><td><strong>43</strong></td><td>41</td><td>26*</td><td>35</td><td>30</td><td>30</td></tr><tr><td>Balkans</td><td><strong>43</strong></td><td>48</td><td>30*</td><td>37</td><td>37</td><td>35</td></tr><tr><td>Anatolia</td><td><strong>48</strong></td><td>60*</td><td>43</td><td>46</td><td>52</td><td>54</td></tr><tr><td>Levant</td><td><strong>59</strong></td><td>75*</td><td>54</td><td>44*</td><td>80*</td><td>75*</td></tr><tr><td rowspan="6">Mean VLDS spatial extent (x10<sup>4</sup> km<sup>2</sup>)</td><td>Maghreb</td><td><strong>11.7</strong></td><td>10.7</td><td>8.8</td><td>10.3</td><td>10.3</td><td>10.2</td></tr><tr><td>Iberian Peninsula</td><td><strong>23.3</strong></td><td>21.1</td><td>23.4</td><td>20.9</td><td>22.4</td><td>23.9</td></tr><tr><td>France</td><td><strong>8.2</strong></td><td>6.7</td><td>5.1</td><td>6.4</td><td>5.8</td><td>6.4</td></tr><tr><td>Italy</td><td><strong>11.8</strong></td><td>13.9</td><td>5.9</td><td>10.5</td><td>8.4</td><td>7.4</td></tr><tr><td>Balkans</td><td><strong>26.1</strong></td><td>24</td><td>21.5</td><td>23.9</td><td>23.7</td><td>20.2</td></tr><tr><td>Anatolia</td><td><strong>7.6</strong></td><td>13*</td><td>9.4</td><td>8.8</td><td>7.4</td><td>8.7</td></tr><tr><td>Levant</td><td><strong>22.9</strong></td><td>23.4</td><td>14.1*</td><td>13.6*</td><td>20.4</td><td>21.1</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 1: Dry spells 80th percentile duration at each grid point of the E-OBS data set (a), and simulated biases for CCLM4 (b), Reg_CM4 (c), ALADIN52 (d), LMDZ4 (e) and LMDZ4-NEMOMED8 (f) compared to E-OBS, over the 1979-2009 period. The grey shaded area indicates the absence of data for the analysis.</figcaption></figure>
<figure><img src="image_4.png" /></figure>

Figure 2: Box plots for VLDS duration (number of consecutive dry days) (a), and for spatial extent (in km2) (b) for E-OBS, CCLM4, Reg_CM4, ALADIN52, LMDZ4 and LMDZ4-NEMOMED8. The bottom and top of the box plots are the first and third quartiles, and the band inside the box is the median. Crosses above box plot indicate extreme values (out of the whisker) while the ends of the whiskers represent the lowest datum still within 1.5 inter-quartile range of the lower quartile, and the highest datum still within 1.5 inter-quartile range of the upper quartile. Stars represent the average: reinforced black for the reference serie E-OBS; for the models runs compare to E-OBS: regular black for the results within the uncertainty range, double stars for the underestimate results (according to the t-test at 0.95 confidence level)
<figure><img src="image_5.png" /><figcaption>Figure 3: Map of the 7 studied Mediterranean sub-regions: the Maghreb, the Iberian Peninsula, France, Italy, the Balkans, Anatolia and the Levant region. The white areas indicate the absence of data for the analysis.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4: Fraction of VLDS days at each grid point with respect to the total studied days in the whole Mediterranean domain computed from the E-OBS data set (a), and simulated biases for CCLM4 (b), Reg_CM4 (c), ALADIN52 (d), LMDZ4 model (e) and LMDZ4-NEMOMED8 model (f). The grey shaded area indicates the absence of data for the analysis.</figcaption></figure>

102x101mm (300 x 300 DPI)
<figure><img src="image_7.png" /></figure>

Figure 5: Observed (black histograms) and simulated (gray histograms: CCLM4; Reg_CM4; ALADIN52; LMDZ4; LMDZ4-NEMOMED8) VLDS seasonality for the 7 Mediterranean sub-regions: Maghreb (a); Iberian Peninsula (b); France (c); Italy (d); the Balkans (e); Anatolia (f); Levant (g). Histograms show the fraction of VLDS beginning in a given month. Numbers above the bars of the histograms indicate the number of VLDS beginning in the given month.
<figure><img src="image_8.png" /></figure>

Figure 6: The five VLDS patterns resulting from hierarchical clustering of the E-OBS data for the Mediterranean Basin: Generalized, North-East, Scattered, South-East and West. The classification of the simulated VLDS events into the 5 observed clusters are shown for CCLM4, Reg_CM4, ALADIN52, LMDZ4 and LMDZ4-NEMOMED8. The color scale indicates the fraction of VLDS events affecting each grid point. The grey shaded area indicates the absence of data for the analysis.