# Modelling extreme precipitation over the Dinaric Alps: An evaluation of the CNRM-ALADIN regional climate model

Sarah Ivušić, Ivan Güttler, Samuel Somot, Jean-françois Guérémy, Kristian Horvath, Antoinette Alias

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## To cite this version:

Sarah Ivušić, Ivan Güttler, Samuel Somot, Jean-françois Guérémy, Kristian Horvath, et al., Modelling extreme precipitation over the Dinaric Alps: An evaluation of the CNRM-ALADIN regional climate model. Quarterly Journal of the Royal Meteorological Society, 2021, 147 (741), pp.4425-4453. 10.1002/qj.4187. hal-04950246

HAL Id: hal-04950246

https://cnrs.hal.science/hal-04950246v1

Submitted on 23 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
DOI: 10.1002/qj.4187

Quarterly Journal of the
Royal Meteorological Society

RMetS

RESEARCH ARTICLE

# Modelling extreme precipitation over the Dinaric Alps: An evaluation of the CNRM-ALADIN regional climate model

Sarah Ivušić<fnref n="1" /> | Ivan Güttler<fnref n="1" /> | Samuel Somot<fnref n="2" /> | Jean-François Guérémy<fnref n="2" />

Kristian Horvath<fnref n="1" /> | Antoinette Alias<fnref n="2" />

**Correspondence**

S. Ivušić, Croatian Meteorological and
Hydrological Service (DHMZ), Zagreb,
Croatia.

Email: sarah.ivusic@cirus.dhz.hr

**Funding information**

The contribution of Sarah Ivušić to this
article was partially funded by HrZZ
(Croatian Science Foundation) contract
I-3833-2016 and by the European Union’s
Horizon 2020 research and innovation
programme under grant agreement no.
653824/EU-CIRCLE.

**Abstract**

One of the Mediterranean hotspots for extreme precipitation is the coastal mountainous eastern Adriatic and Dinaric Alps regions, which are often affected by heavy precipitation events (HPEs) that can cause severe damage. Representing these events at different time scales and projecting their future evolution using regional climate models (RCMs) remains a key modelling challenge. This study evaluates the impact of model configuration on the representation of extreme daily precipitation in an RCM at climatological (1979–2012) and event scales (HPEs). Additionally, the impact of the spectral nudging (SN) technique is analysed. We compare two CNRM-ALADIN model configurations, and perform several sensitivity tests on specific parameters within a configuration. All simulations are driven by the ERA-Interim re-analysis over the Med-CORDEX domain at 0.11° horizontal resolution. On all examined time scales, model configuration shows a considerable impact on the mean and extreme daily precipitation. The new physical parameterizations of moist processes show improvement at the climatological (precipitation intensity, extreme precipitation and frequency of light precipitation) and event (the occurrence, spatial pattern and structure of HPEs) scales. Extreme precipitation shows limited sensitivity to specific parameters and is highly dependent on HPE. The use of SN improves the temporal variability at climatological scales and the location and occurrence of HPEs. We conclude that extreme precipitation representation in CNRM-ALADIN is more sensitive to change in the model configuration, particularly in the physical parameterizations, than to the application of SN. This study shows that the development and advancement of physical parameterizations can improve the model representation of extreme precipitation at several time scales and can therefore be considered as a means to reduce uncertainties in future climate projections.

**KEYWORDS**

heavy precipitation event, CNRM-ALADIN, Dinaric Alps, HyMeX, Med-CORDEX, model physics, precipitation, regional climate model

<footnote n="1">¹Croatian Meteorological and
Hydrological Service (DHMZ), Zagreb,
Croatia</footnote>

<footnote n="2">²Centre National de Recherches
Météorologiques (CNRM), Université de
Toulouse, Météo-France, CNRS, Toulouse,
France</footnote>
# 1 | INTRODUCTION

Heavy precipitation events (HPEs) are one of the most threatening weather phenomena in the Mediterranean, frequently leading to flash floods, landslides, traffic interruptions, water and electricity supply problems and human casualties. The Dinaric Alps and Adriatic regions are no exception to hazards caused by HPEs (Ruti et al., 2016). HPEs are most common in autumn when the sea surface temperature is still high, leading to large thermal differences between the sea and atmosphere (e.g., Ivančan-Picek et al., 2016). Triggering mechanisms of all HPEs occurring in the Adriatic are similar. On a synoptic scale, a deep upper-level trough extends from northern Europe to the Mediterranean, frequently followed by lee cyclogenesis (Horvath et al., 2008), and on a mesoscale, prefrontal advection of moist and warm air from the sea by south-easterly low-level flow. This flow, often organized in a convergence line, is then forced to rise due to the steep orography, thus triggering or reinforcing convection and generating, usually, prefrontal orographic precipitation and enhancing frontal precipitation (e.g., Kozarić and Ivančan-Picek, 2006; Ivančan-Picek et al., 2014; Davolio et al., 2016). The HPEs observed during the first Hydrological cycle in the Mediterranean Experiment (HyMeX; Ducrocq et al., 2014) Special Observation Period (SOP1) developed under similar conditions (Ivančan-Picek et al., 2016). During the SOP1, the northern Adriatic was one of the most affected areas, with more than 1,000 mm of precipitation recorded at several stations. Eight intensive observation periods (IOPs) were characterized by precipitation over 100 mm day⁻¹, and during IOP2, the measured precipitation reached 220 mm day⁻¹. Consequently, the forecast of intense events like these, specifically over the coastal mountainous region, presents a great challenge for the numerical weather prediction (NWP) models, mainly due to the parameterized moist physical processes.

The coastal mountainous region in the eastern Adriatic is also characterized by significant climate extremes (Frei and Schär, 1998; Isotta et al., 2014a). In the Dinaric Alps (Figure 1), specifically, the areas of the Gorski Kotar and Velebit mountain range and Konavle Plain beneath the Montenegrin Mountains, the mean annual precipitation ranges from 3,000 to 3,500 mm. On the north-eastern slopes of the Konavle Plain lies the wettest area in the Mediterranean, the Krivošije area, with 5,000 mm of annual precipitation (Ducić et al., 2012), where, on average, it rains 50% of the days in a year (Zaninović et al., 2008).

The extreme precipitation trends over the 1961–2010 period are weak but still show a slight increase during autumn over the Croatian mainland and a decrease

during summer over the coastal mountainous region (Gajić-Čapka et al., 2015). A negative trend was found for summer and annual precipitation amounts over the Croatian coastal mountainous region and their surrounding areas. A decrease in the annual number of days with precipitation occurred in the Krivošije region during the 1951–2007 period (Ducić et al., 2012).

Similar tendencies have been projected to continue towards the end of the twenty-first century in numerous studies based on future projection simulations for the Mediterranean using regional climate models (RCMs). The reduction in total precipitation and decrease in the number of wet days contrasts with the increase in extreme precipitation intensity (Sillmann et al., 2013; Jacob et al., 2014; Rajczak and Schär, 2017; Drobinski et al., 2018; Tramblay and Somot, 2018). Furthermore, an increase in the frequency of environments favourable for severe convective storms can be expected by the end of the century (Púčik et al., 2017), especially on the Mediterranean coast, mostly due to the increase in sea surface temperature (Kahraman et al., 2020). However, several studies have emphasized that there are considerable uncertainties among climate projections (e.g., Déqué et al., 2007; Rajczak and Schär, 2017), and one of the sources of the uncertainties is the RCMs themselves (e.g., Giorgi, 2019). Primarily, the causes of these uncertainties between RCMs are the physical parameterizations of the subgrid processes, especially the convection parameterization schemes (Déqué et al., 2007; Prein et al., 2013; Katragkou et al., 2015; Rajczak and Schär, 2017), which play a major role in simulating the summer and autumn extreme precipitation. Improving model physical parameterizations is therefore often presented as one of the approaches to reduce the overall uncertainties in climate projections (Hawkins and Sutton, 2009). The effort put into the assessment of RCM abilities in the present climate and, in particular, the evaluation of the physical parameterizations is thus fundamental in progressing towards a reduction in uncertainty.

Evaluation of RCMs is one of the main goals of the Coordinated Regional Downscaling Experiment (CORDEX; Giorgi et al., 2009) initiative. The evaluation of the latest high-resolution regional climate multi-model ensembles over Europe (EURO-CORDEX; Jacob et al., 2014) and the Mediterranean (Med-CORDEX; Ruti et al., 2016) shows that RCMs realistically simulate mean precipitation, precipitation spatial patterns and temporal variability (Kotlarski et al., 2014; Fantini et al., 2018), but the frequency and intensity of light and extreme precipitation are poorly represented (Sun et al., 2006; Boberg et al., 2010; Kjellström et al., 2010; Fantini et al., 2018). Namely, RCMs excessively produce light rain and underestimate extreme precipitation amounts
**FIGURE 1** Map of the study domain (D; black rectangle) with CNRM-ALADIN topography (part of the med-CORDEX domain) with the subdomains (D1, D2 and D3; red shapes). Black and blue dots represent stations used for MESCAN on September 12, 2012. Red dots show additional stations not used to build MESCAN. Stations with indicated names represent those used for Q-Q plots (black- and red-coloured names) and the radiosounding station (blue-coloured name). Light-green labels show mountains, mountain ranges and mountainous regions, and the plains are marked in dark green [Colour figure can be viewed at wileyonlinelibrary.com]

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and frequency, especially in areas with complex orography (Frei *et al.*, 2003; Fantini *et al.*, 2018).

Furthermore, the technique of nesting RCMs in global models, or dynamical downscaling, is another source of uncertainty that needs to be acknowledged when evaluating RCMs and is rarely investigated. One of the approaches in dynamical downscaling is the use of the spectral nudging (SN) technique, which forces RCMs to adopt large scales of the driving global climate model over the entire domain, not only at the lateral boundaries or in a relaxation zone. In addition, SN allows small-scale features to freely develop by the RCM (Waldron *et al.*, 1996). SN has proven to successfully eliminate the effect of domain position and geometry, and it is preferable for applications that involve relatively large domains (Miguez-Macho *et al.*, 2004). The SN does not limit the RCM skill in simulating precipitation extremes, especially when specific humidity is nudged (Radu *et al.*, 2008; Colin *et al.*, 2010). The utilization of SN shows improvements in temporal characteristics and chronology for open-ocean deep convection event in north-western Mediterranean (Herrmann and Somot, 2008), daily wind speed and intense wind events over the Mediterranean Sea (Herrmann *et al.*, 2011) and in the study of dust aerosol radiative effects over the Mediterranean (Nabat *et al.*, 2015a).

Studies evaluating RCMs on specific well-documented cases are rare in the literature despite the high potential for model evaluation and a large number of well-observed HPE cases. A need to understand the behaviour of RCMs at the event scale is two-fold. First, to recognize the sources of their biases in order to acknowledge the RCM's strengths and weaknesses (Khodayar *et al.*, 2016) and, second, to increase confidence in RCM projection simulations and their respective climate change signals (Kendon *et al.*, 2014), especially considering extreme precipitation. Examples of case-study-oriented evaluations of RCMs can be found in Herrmann and Somot (2008), Nabat *et al.* (2015b) and Khodayar *et al.* (2016). The last study focused on Mediterranean HPE, showing that RCMs underestimate the precipitation amount of HPE with a structure that is too broad at the daily scale, and at a subdaily scale, they produce a great number of long-lived, weak-intensity events. These deficiencies come from the errors in the vertical distribution of moisture and parameterized processes that initiate deep convection.

From all of the abovementioned results, it follows that a large fraction of the errors and uncertainties in RCM performance when considering precipitation come from the physical parameterization schemes, regardless of
the examined time scale. This sensitivity of precipitation representation to parameterizations is, however, rarely assessed in the RCM evaluation literature thus far. Therefore, the main objectives of this study are:

* To examine the impact of RCM physics configuration on the representation of daily precipitation, with a particular focus on the extremes at the climatological and event (i.e., HPE) temporal scales and, additionally, to analyse the impact of the SN technique;

* To investigate the sensitivity of extreme precipitation at the event scale to specific parameters in physical parameterization schemes.

We evaluate RCM simulations from two CNRM-ALADIN versions with different model configurations and physical parameterization packages, including convection, turbulence and microphysics schemes. The domain of interest encompasses the region of the eastern Adriatic and Dinaric Alps, an area usually underrepresented in model evaluation and rarely considered as a whole. To our knowledge, there has not been a comprehensive attempt to address these issues over this particularly complex region, especially with the use of an extensive number of evaluation methods and over several time scales.

The article is organized as follows: the model versions, observational datasets and evaluation methods are presented in Section 2; the results are analysed and discussed in Section 3; and finally, the conclusions are addressed in Section 4.

## 2 | METHODOLOGY

### 2.1 | CNRM-ALADIN regional climate model

We use the limited area model ALADIN (Aire Limitée Adaptation dynamique Développement InterNational; Bubnová et al., 1995) in its regional climate version CNRM-ALADIN (Spiridonov et al., 2005; Déqué and Somot, 2008; Radu et al., 2008). CNRM-ALADIN is a bispectral model that has a semi-implicit, semi-Lagrangian advection scheme. Despite the spectral nature of CNRM-ALADIN, the nonlinear contributors in equations, that is, the parameterizations, are computed in a grid-point space. CNRM-ALADIN uses a 2D bispectral Fourier decomposition (Haugen and Machenhauer, 1993) to compute horizontal diffusion, semi-implicit corrections and horizontal derivatives. Boundary conditions are implemented by the use of the Davies method (Davies, 1976). On the entire domain, the conversion spectral-to-grid-point

space is accomplished with fast Fourier transforms (Farda et al., 2010; Herrmann et al., 2011).

Two versions of CNRM-ALADIN are evaluated in this study: version 5.2 (Colin et al., 2010) and the newest version, 6.3 (Daniel et al., 2019; Nabat et al., 2020). The main difference between versions 5.2 and 6.3 is in their physical parameterizations, namely, in the latest version, the physical parameterization schemes are substantially changed (Table 1), particularly the parameterizations of moist processes that convection, turbulence and microphysics schemes address (Roehrig et al., 2020). The new convection scheme in version 6.3, called Prognostic Condensates, Microphysics and Transport (PCMT), is based on the work of Piriou et al. (2007) and Guérémy (2011). This scheme provides a consistent, continuous and prognostic treatment of convection from dry thermals to deep precipitating events. In comparison, the layer underneath the level of free convection was not taken into account in the former convection scheme (Bougeault, 1985). The consistency in the new convection scheme is ensured by the use of buoyancy in the definition of the mass flux and the closure condition, while in the former, the mass flux is expressed according to a moist static energy formulation and a moisture convergence closure. Furthermore, convective condensation enters the same prognostic microphysics scheme as stratiform processes, giving rise to prognostic convective water contents (Lopez, 2002), while all condensed water precipitates in the former microphysics scheme (Smith, 1990). The new microphysics scheme also includes collection, sedimentation and evaporation processes. The turbulence scheme follows the approach of Cuxart et al. (2000), which represents the turbulent kinetic energy (TKE) with a 1.5-order scheme prognostic equation. The non-local mixing length is based on Bougeault and Lacarrere (1989). This scheme also diagnoses the grid-scale variance of the local distance to saturation (expressed in terms of the total water and liquid-water potential temperature turbulent fluctuations), which in turn allows the computation of both stratiform cloudiness and water content in a consistent way, following the work of Bougeault (1981). This resulting stratiform water content defines the corresponding condensation tendency that enters the microphysics scheme, giving rise to prognostic stratiform water contents (the former microphysics scheme being diagnostic). A more detailed overview of the CNRM-ALADIN physics update is provided by Roehrig et al. (2020) and Nabat et al. (2020).

CNRM-ALADIN version 5.2 uses the standard version of the Interaction Sol-Biosphère-Atmosphère (ISBA; Noilhan and Planton, 1989) land-surface model, which has no tiles, indicating that a grid point is either sea or land. CNRM-ALADIN version 6.3 uses the multi-layer diffusion-based ISBA version (Boone et al., 2000;
<figure><img src="image_4.png" /></figure>

<table><caption>TABLE 1 CNRM-ALADIN version 5.2 and version 6.3 parameterizations and model set-up</caption><thead><tr><th>CNRM-ALADIN version</th><th>Convection</th><th>Micro-physical scheme</th><th>Shortwave/longwave radiation</th><th>Turbulence</th><th>Clouds</th><th>Land surface model</th><th>Number of vertical levels</th><th>Time steps (s)</th></tr></thead><tbody><tr><td>5.2</td><td>Bougeault (1985)</td><td>Smith (1990)</td><td>Morcrette (1989)</td><td>Ricard and Royer (1993)</td><td>Ricard and Royer (1993)</td><td>ISBA land-surface model (no tile); Noilhan and Planton (1989)</td><td>31</td><td>600</td></tr><tr><td>6.3</td><td>Guérémy (2011), Piriou et al. (2007)</td><td>Lopez (2002)</td><td>Morcrette et al. (2008), Mlawer et al. (1997)</td><td>Cuxart et al. (2000), Bougeault and Lacar-rere (1989)</td><td>Bougeault (1981)</td><td>SURFEX v8 platform (includes tile); Decharme et al. (2019), Masson et al. (2013)</td><td>91</td><td>450</td></tr></tbody></table>

Decharme *et al.*, 2019) as part of the land surface modelling platform SURFEX (Masson *et al.*, 2013; Decharme *et al.*, 2019), which uses tiles and distinguishes between the natural land surface, town, lake and sea.

The two CNRM-ALADIN versions are driven by ERA-Interim (Dee *et al.*, 2011) over the Med-CORDEX domain (www.medcordex.eu; Ruti *et al.*, 2016). Monthly SST and 6-hourly lateral boundary conditions are applied. Operating on the Lambert conformal projection with a horizontal resolution of ~12.5 km (0.11°, MED-11), CNRM-ALADIN covers the area 25–57°N and 23°W–53°E with 405 grid points in longitude and 261 grid points in latitude, not including the 2 × 8-point wide relaxation and 11-point biperiodization zones. The numbers of vertical levels are 31 and 91, and the time steps are 600 s and 450 s for CNRM-ALADIN versions 5.2 and 6.3, respectively (Table 1).

For evaluation at the climatological scale, three simulations are used (Table 2): (a) ALD52: CNRM-ALADIN version 5.2 without SN, (b) ALD52n: CNRM-ALADIN version 5.2 with SN, and (c) ALD63n: CNRM-ALADIN version 6.3 with SN. The nudged variables, including their e-folding times, are temperature (24 hr), surface pressure (24 hr), specific humidity (24 hr), wind vorticity (6 hr) and wind divergence (48 hr; Colin *et al.*, 2010). Wavelengths

below 300 km are not nudged, and those above 400 km are fully nudged with the linear transition in between. Variables below 880 hPa are not nudged, whereas those above 750 hPa are fully nudged with the linear transition in between (Radu *et al.*, 2008). Comparison of ALD52 and ALD52n addresses the effects of SN, while analysis of ALD52n and ALD63n examines the effects of the new model configuration.

For analysis at the event scale, the same simulations are used as in the analysis at the climatological scale for version 5.2 (ALD52 and ALD52n). However, for ALD63n, we perform additional sensitivity tests (Table 2). Again, the simulations are spectrally nudged and are forced by the same initial and lateral boundary conditions, but they cover only 2012. The aim of these specific additional tests at the event scale is to assess the impact of two parametric options of the new physics: the one used in the global scale counterpart of CNRM-ALADIN version 6.3, ARPEGE for CMIP6, and the one used in the Météo-France operational seasonal forecast (Guérémy, 2011), which includes more recent additional developments. The tests are conducted by modifying the following parameters:

1. Convection (I): convective entrainment and aerodynamic drag increased together with surface

<table><caption>TABLE 2 CNRM-ALADIN simulation details</caption><thead><tr><th>Simulation</th><th>CNRM-ALADIN version</th><th>Spectral nudging</th><th>Period</th></tr></thead><tbody><tr><td>ALD52</td><td>5.2</td><td>No</td><td>January 01, 1979 to December 31, 2012</td></tr><tr><td>ALD52n</td><td>5.2</td><td>Yes</td><td>January 01, 1979 to December 31, 2012</td></tr><tr><td>ALD63n</td><td>6.3</td><td>Yes</td><td>January 01, 1979 to December 31, 2012</td></tr><tr><td>ALD63n-t1, ALD63n-t2, ALD63n-t3</td><td>6.3</td><td>Yes</td><td>January 01, 2012 to December 31, 2012</td></tr></tbody></table>
fluxes (subgrid scale variability effects included) with marginal microphysics and radiation modifications;

2. Turbulence (II): increased in the presence of convection through the inclusion of convective transport fluxes in the TKE production terms;

3. Dynamics (III): 3D monotonic semi-Lagrangian interpolations for wind, temperature and, more importantly, specific humidity, instead of only horizontal 2D monotonic semi-Lagrangian interpolations giving a possible rise of overshoot in the vertical.

These modifications are introduced gradually from the ALD63n parametric option to the one used in the seasonal forecast. The sensitivity tests are conducted as follows:

1. ALD63n-t1: I

2. ALD63n-t2: I + II

3. ALD63n-t3: I + II + III, being the parametric option used in the Météo-France seasonal forecast.

## 2.2 | MESCAN

MESCAN is a regional precipitation analysis system (Soci et al., 2016) developed as part of the European Reanalysis and Observations for Monitoring (EURO4M) project, and it continues to develop under the Uncertainties in Ensembles of Regional ReAnalyses (UERRA) project as MESCAN-SURFEX (further on, only MESCAN). MESCAN runs the optimal interpolation algorithm on observations with a first guess or so-called background field coming from a downscaled regional re-analysis. In the case of precipitation, the background field is a short-range hindcast from the NWP system HARMONIE-ALADIN. The hindcast was forced with ERA-Interim or ERA-40 fields and initialized at 0000, 0600, 1200 and 1800 Universal Time Coordinated (UTC) over Europe with three-dimensional variational data assimilation (3D-Var). The hindcast is then statistically downscaled from the original 11 km resolution to 5.5 km using a 12-point cubic interpolation technique. Observational data from the European Climate Assessment Dataset (ECA&D) and ECMWF databases were obtained, including several national high-resolution rain gauge networks (Sweden, Norway and France). MESCAN precipitation analysis is available over Europe at a 5.5 km resolution every 24 hr at 0600 UTC for the 1961 to August 2018 period.

It is worth noting that the MESCAN is based on a model background coming from the HARMONIE-ALADIN model. Although the HARMONIE-ALADIN used in MESCAN and the CNRM-ALADIN used in this study are different in terms of configuration (domain, resolution, running mode and physical parameterization), we

underline that our reference dataset and the evaluated model are probably not fully independent. Furthermore, the accuracy of MESCAN directly depends on rain gauge station density. Isotta et al. (2014b) showed that MESCAN successfully corrects the biases of its background field in station dense areas, but it overestimates the lowest frequencies and underestimates the extreme event intensity where the observation data are sparse. Despite these deficiencies, MESCAN is currently the most suitable reference dataset given the lack of high-resolution gridded products covering the selected domain (Figure 1).

## 2.3 | Observational data

### 2.3.1 | Surface observations

The daily precipitation observations were taken from two sources, ECA&D and ECMWF databases, used for the construction of the MESCAN. Note that some stations are in both datasets. First, for the evaluation at the climatological scale, the observations for several meteorological stations were retrieved from the ECA&D database (Klein Tank et al., 2002). Almost all selected stations are located in Croatia, except for one in Bosnia and Herzegovina; namely, only these stations have the time series for the required time period and are accessible to download (Figure 1; the stations with the station name in black colour). Additionally, the daily precipitation data were obtained for two stations not used to build MESCAN: Herceg Novi and Crkvice (Figure 1; the stations marked in red colour), from the Krivošije region in Montenegro.

Second, for the evaluation of the model at the event scale, the SYNOP and climatological station data from the ECMWF operational archive are used. The availability of meteorological station data changes each day depending on different factors. As an example, the data available on September 12, 2012, are displayed in Figure 1 (stations labelled with black and blue dots).

### 2.3.2 | Radiosoundings

The radiosounding measurements obtained from station Zadar-Zemunik (station ID 14430; Figure 1) are available at 0000 and 1200 UTC. For several selected HPEs that had mostly affected the northern Adriatic coast, the selected radiosounding is not the most representative of the upstream preconvective environment because of its location in the central part of the Croatian eastern Adriatic coast. However, it is the only available station in the eastern Adriatic, and it approximately satisfies a definition
of proximity sounding (Rasmussen and Blanchard, 1998); namely, it is released within 400 km of the event at least 6 hr before the event.

**2.4 | The evaluation methods**

The study domain encompasses a broader region of the Dinaric Alps mountain range, 13.2°–20.9°E, 41.9°–46.7°N (Figure 1). The domain also includes the south-western part of the Pannonian Plain and a large part of the Adriatic Sea with its highly complex eastern coast. Furthermore, three subdomains are selected following their specific climatological and topographic characteristics. The first subdomain, D1, encompasses the northern Adriatic coast, Gorski Kotar and the Velebit and Kapela mountain ranges. This area is under the influence of a large number of cyclones that form in the Gulf of Genoa and northern Adriatic, causing frequent and, together with orographic influences, large amounts of rainfall (e.g., Horvath et al., 2008). The second subdomain, D2, includes mostly the Montenegrin Mountains, including the rainiest area in the Mediterranean, the Krivošije region of the Orjen Mountain. The abundant precipitation in this region develops under similar conditions as in D1, and here, the orographic effects of blocking the cyclonic systems are very pronounced (Tošić, 2004). The third subdomain, D3, covers the south-western part of the Pannonian Basin characterized by low annual precipitation amounts because mainly dry air masses are advected over the area (Zani nović et al., 2008; Ceglar et al., 2018).

The evaluation of CNRM-ALADIN is carried out separately for two periods. First, the climatological period from 1979 to 2012 (34 years) is the longest available period for which all versions of CNRM-ALADIN have daily precipitation data. Second is the HyMeX SOP1 period, September 5 to November 6, 2012, during which several HPEs seriously affected the eastern Adriatic and Dinaric Alps. For both periods of the evaluation, we use daily precipitation (for the model, re-analysis and observations), which accumulates from the morning of the specific day at 0600 UTC until 0600 UTC on the following day (for example, the precipitation on September 12 is the rainfall accumulated from September 12 at 0600 UTC until September 13 at 0600 UTC). Furthermore, as the emphasis will be on simulated climatological and meteorological extremes, the goal is to keep CNRM-ALADIN close to its original spatial resolution and not to “smooth out” the local maxima of precipitation. Both CNRM-ALADIN and MESCAN are remapped using the nearest neighbour method to the regular grid 0.125°×0.125° in all analyses applied. Conservative remapping method was also tested, and

results showed no major sensitivity to the interpolation procedure.

**2.4.1 | The evaluation methods for the climatological scale**

To assess the model's ability to represent the precipitation climatology of the area, we use standard performance metrics, including mean annual and seasonal precipitation, with corresponding biases. For seasonal means, the agreement between the observed and modelled spatial patterns is examined using the spatial correlation, spatial standard deviation and centred root-mean-square difference (RMSD) jointly summarized in the form of the spatial Taylor diagram (Taylor, 2001). The annual cycle for the three selected subdomains is determined as spatially averaged mean precipitation for each month. To examine the annual cycle in more detail, we employ two additional metrics: climatological rank correlation (CRCO) and the ratio of yearly amplitudes (ROYA). For CRCO, the Spearman rank correlation is calculated between the observed and modelled spatially averaged monthly mean precipitation, while ROYA represents the ratio of modelled and observed yearly amplitudes (e.g., Kotlarski et al., 2014).

To target the CNRM-ALADIN skill in reproducing the daily precipitation, we examine the probability distribution function (PDF) by pooling out all grid-point values within each subdomain separately for every day during the 1979–2012 period and grouping them into non-uniform wide bins. Additionally, the daily precipitation data of CNRM-ALADIN, together with MESCAN, are directly compared with rain gauge data (ECA&D and two additional stations with names indicated in black and red, respectively, in Figure 1) in the form of quantile-quantile plots. Data are extracted from only one grid point that best matches the location and altitude of each station.

Furthermore, we analyse several precipitation indices based on daily precipitation:

1. WDF: wet-day frequency (percentage of wet days per year);

2. SDII: simple daily intensity index (mm day⁻¹);

3. R99p<sub>OBS</sub>: extremely wet days (percentage of extremely wet days per total number of wet days);

4. P99p<sub>OBS</sub>: precipitation amount due to extremely wet days (percentage of precipitation amount on extremely wet days per total precipitation amount on wet days).

A wet day is considered to be the day when the precipitation amount is greater than or equal to 1 mm. An extremely wet day is a wet day with daily precipitation
<figure><img src="image_5.png" /></figure>

<table><caption>TABLE 3 Intense observation periods (IOPs) date, area affected (Ivančan-Picek *et al.*, 2016) and selected radiosounding</caption><thead><tr><td>IOP</td><td>Date</td><td>Area affected</td><td>Radiosounding</td></tr></thead><tbody><tr><td>2</td><td>September 12–13, 2012</td><td>North Adriatic coast, Gorski Kotar, Rijeka</td><td>September 12, 2012, 0000 UTC</td></tr><tr><td>4</td><td>September 13–14, 2012</td><td>Pelješac (a peninsula near Dubrovnik)</td><td>September 13, 2012, 0000 UTC</td></tr><tr><td>13</td><td>October 14–16, 2012</td><td>Rijeka, Karlobag, Imotski and islands Hvar and Mljet</td><td>October 15, 2012, 0000 UTC</td></tr><tr><td>16</td><td>October 26–28, 2012</td><td>Rijeka, Rijeka inland</td><td>October 27, 2012, 0000 UTC</td></tr><tr><td>18</td><td>October 31 to November 2, 2012</td><td>Istria, Rijeka</td><td>October 31, 2012, 0000 UTC</td></tr><tr><td>19</td><td>November 4–5, 2012</td><td>Rijeka inland</td><td>November 4, 2012, 0000 UTC</td></tr></tbody></table>

higher than the 99th percentile of precipitation estimated for wet days only (Klein Tank *et al.*, 2009). WDF and SDII are commonly used indices, either with rain gauge data (Alexander *et al.*, 2006; Ducić *et al.*, 2012; Gajić-Čapka *et al.*, 2015) or with modelled precipitation fields (Frei *et al.*, 2003; Sillmann *et al.*, 2013; Fantini *et al.*, 2018). Furthermore, all selected indices are included in World Meteorological Organization (WMO) guidelines for the analysis of extremes under a changing climate (Klein Tank *et al.*, 2009), except that (3) and (4) are slightly adapted. Indices R99p<sub> OBS</sub> and P99p<sub> OBS</sub> are modified compared with the standard definitions R99 and R99pTOT, respectively. R99 is the number of extremely wet days, and R99pTOT is the sum of precipitation on those extremely wet days. In this original concept, the 99th percentile threshold is calculated separately for observations and models, while here we consider the 99th percentile threshold from observed daily precipitation (MESCAN) applied to both observations and models (Fantini *et al.*, 2018).

## 2.4.2 | The evaluation methods at the event scale

For the investigation of CNRM-ALADIN skill in reproducing the extreme precipitation at the event scale, that is, the HPE, we focus on six IOPs during HyMeX SOP1 (Drobinski *et al.*, 2014; Ivančan-Picek *et al.*, 2016). The details about the IOPs, their duration and area they affected, are presented in Table 3. The spatial distribution of precipitation in CNRM-ALADIN simulations is evaluated against MESCAN and the station data used to build MESCAN.

For further evaluation of HPEs, we apply the Structure Amplitude Location (SAL) method (Wernli *et al.*, 2008) to verify the CNRM-ALADIN daily precipitation against MESCAN for each day of the selected HyMeX IOPs. SAL recognizes precipitation objects as opposed to the more classical verification measures that compare corresponding grid points (Wernli *et al.*, 2009). SAL differentiates

between three components of precipitation: structure (S), amplitude (A) and location (L). The domain of interest must be predefined, and objects within the domain are identified by the threshold value. We use the recommended threshold value of 1/15 of the maximum precipitation within the domain. The method requires the model and observations to be on the same spatial grid, and objects are determined separately for the model and observations. Component A is a relative difference of the domain-averaged precipitation. Component A values range within [-2, 2], and positive (negative) A values indicate that the model overestimates (underestimates) total precipitation. For example, precipitation is overestimated by a factor of 2 for A = 0.67. The S component compares the volume of precipitation objects, that is, it captures the information about the size and shape of the objects. S values also range within [-2, 2], where positive (negative) S values indicate that modelled precipitation objects are too large and/or too flat (too small and/or too peaked). This is closely related to the physical characteristics of precipitation, where the model could show more convective or stratiform structures. The L component examines two parts: the first part compares the centres of mass of the total precipitation field, and the second part compares the centres of mass of separate objects with one of the total precipitation objects. L ranges within [0, 2]. The modelled precipitation is considered to be perfect when S, A and L are zero.

Finally, CNRM-ALADIN's representation of the preconvective environment at the subdaily scale is analysed for sensitivity tests with ALD63n. For HPEs, the accurate representation of the local storm dynamics is essential (Kendon *et al.*, 2014); thus, atmospheric stratification, stability conditions and vertical wind shear are analysed for the ALD63n sensitivity tests. Vertical profiles of temperature and humidity are compared with the radiosounding measurements for each IOP. The profiles are extracted from the Zadar-Zemunik station from a single grid point at 0000 UTC on the day with maximum precipitation. Furthermore, the observed
radiosounding and instantaneous vertical profiles of tem-
perature, specific humidity, wind and pressure extracted
from ALD63n sensitivity tests provided input to the
Sounding and Hodograph Analysis and Research Program
in Python (SHARPpy) software (Blumberg et al., 2017).
With SHARPpy, several thermodynamic quantities were
derived: convective available potential energy (CAPE),
convective available potential energy integrated up to a
3 km height (CAPE3), convective inhibition (CIN), lifted
index (LI) and K-index. All indices depend on the choice of
level from which the parcel will be lifted (except K-index),
and here we selected the surface-based parcel. CAPE rep-
resents the maximum possible amount of energy an air
parcel can have for vertical acceleration due to buoyancy
and thus is related to the potential updraft strength of
a thunderstorm (Moncrieff and Miller, 1976). CIN repre-
sents the energy required for an air parcel to rise adiabati-
cally from its initial position to the level of free convection
(Colby, 1984). Conditions may be favourable for the devel-
opment of convection, such as high CAPE, but if CIN is
sufficiently large, deep convection will not form. LI is cal-
culated as the difference between the environment and
parcel temperature at 500 hPa. Negative values of LI indi-
cate that the parcel has positive buoyancy and that there
is a potential for severe weather. The K-index is calculated
as (T850 - T500) + Td850 - (T700 - Td700), where T is
the temperature and Td is the dewpoint at the pressure
levels indicated. The higher the moisture is, the greater
the 850-500 temperature difference, and thunderstorms
are likely to occur when K-index > 20 °C. SHARPpy is
used to calculate bulk wind shear, that is, the magnitude of
the wind vector difference between the surface and 1 km
(SHEAR 0-1 km), surface and 3 km (SHEAR 0-3 km) and
surface and 6 km (SHEAR 0-6 km).

3 | RESULTS AND DISCUSSION

3.1 | The evaluation of the 1979–2012 period

3.1.1 | Mean precipitation

Mean annual precipitation is largely affected by proximity
to the sea and orography of the Dinaric Alps (Figure 2a).
It is lowest over the south-western part of the Pannonian
Plain, gradually increasing towards the inland mountains
and mountainous hinterland, and largest values over the
highest peaks. All CNRM-ALADIN simulations show sim-
ilar spatial variability and are in close agreement with
MESCAN (Figure 2d–f). ALD63n produces higher precipi-
tation amounts over the mountain ranges than MESCAN,
particularly over the Gorski Kotar and Velebit, mountains

of central Bosnia and Herzegovina and Montenegrin lit-
toral mountains (Figure 2i). However, ALD63n underesti-
mates the annual precipitation over Slovenia and central
Croatia, and a similar bias is found in ALD52 (Figure 2g).
This bias is not found in ALD52n (Figure 2h), and it
appears to be eliminated since SN causes overestimation
over the entire domain. Table 4 confirms the previous
results. ALD63n has the smallest bias (slightly negative);
ALD52 follows with a small positive bias and ALD52n with
a very large positive bias over the entire domain. All simu-
lations have a high spatial correlation coefficient. SN leads
to an increase of up to 20% in annual precipitation in the
former model configuration (Figure 2b), while the new
model configuration appears substantially drier over the
lowlands and the sea despite the use of SN (Figure 2c).

The mean precipitation in all seasons is shown in
Figure 3a for MESCAN. Autumn and winter are the
seasons with the highest mean precipitation over the
Dinaric Alps. Accordingly, the rest of the seasons are drier,
especially summer. Over the Pannonian Basin, the situa-
tion is reversed. ALD52 shows underestimation over the
north-western part of the domain in summer and autumn,
pronounced overestimation over the Montenegrin high
plains and mountains in winter and overestimation dur-
ing spring over the entire domain (Figure 3b). ALD52 has
the smallest bias and RMSE over the entire domain (except
in spring) and D2 (Tables S1–S4). Again, ALD52n is alto-
gether wetter than ALD52 due to SN, especially in summer
and over the sea (Figures 3c and S1a–d). Over D1, in most
seasons, ALD52n presents the best simulation regarding
the overall results for the bias, RMSE and spatial correla-
tion (Tables S1–S4). For all seasons, ALD63n shows over-
estimation only over the mountain ranges, except for sum-
mer, when underestimation is present over both highlands
and lowlands (Figure 3d). Accordingly, ALD63n shows
the smallest RMSE and largest spatial correlation over D3
(Tables S1–S4). The new model configuration is substan-
tially wetter over the mountains in autumn and especially
winter, while presenting drier conditions in spring, consid-
erably during summer over the entire domain and over the
sea in all seasons (Figure S1e–h).

Taylor diagrams estimated for the mean seasonal pre-
cipitation (Figure 4) show very high spatial correlation
coefficients, between ~0.85 and 0.9, for all simulations.
ALD52 and ALD52n show similar standard deviations
in all seasons, although slightly larger in SN simulation.
In all seasons, ALD63n shows larger spatial variability
than MESCAN, ALD52 and ALD52n, except in summer.
Therefore, the RMSD is somewhat larger for ALD63n than
for ALD52 and ALD52n.

In contrast to spatial Taylor diagrams, the tempo-
ral Taylor diagrams (Figure S2) show that ALD52 and
ALD52n are not very similar (except in summer). In most
<figure><img src="image_6.png" /><figcaption>FIGURE 2 Mean annual precipitation during the 1979–2012 period for (a) MESCAN, (d) ALD52, (e) ALD52n and (f) ALD63n with the corresponding relative biases for (g) ALD52, (h) ALD52n and (i) ALD63n and mean annual precipitation relative differences between (b) ALD52n and ALD52, and (c) ALD63n and ALD52n [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

cases, SN simulations (ALD52n and ALD63n) are better correlated with observations than the simulation without nudging (ALD52). The reason for this is that SN adopts large scales over the full Med-CORDEX domain, not only at boundaries. SN simulations also show very similar results compared with each other, except in summer. In contrast to other seasons, in summer, ALD63n has the smallest correlation. Limitations found in the new model configuration during summer may come from the strong impact of physical parameterizations causing it to diverge from the large-scale fields and to reduce temporal correlation.

Figure 5 presents the annual cycle of mean precipita-
tion over the 1979–2012 period within each subdomain.
The observed annual cycle shows two similar annual
courses over D1 and D2, a maritime type of annual cycle
with minimum precipitation occurring in summer and
autumn as the rainiest season (Figure 5a,b). ALD52 over-
estimates precipitation in spring over D1, while over D2,
overestimation is also present in winter. During summer
and autumn, an underestimation is displayed within both
subdomains. ROYA shows slight overestimation of annual
amplitude. According to previous results, ALD52n shows
an overestimation of monthly mean precipitation over D1
<figure><img src="image_7.png" /></figure>

<table><caption>TABLE 4 Mean annual precipitation bias, root mean square error (RMSE) and spatial correlation coefficient (SCC) for ALD52, ALD52n and ALD63n estimated against MESCAN over the entire domain (D) and three subdomains (D1, D2, D3)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Bias (%)</th><th colspan="4">RMSE (mm)</th><th colspan="4">SCC</th></tr><tr><th>D</th><th>D1</th><th>D2</th><th>D3</th><th>D</th><th>D1</th><th>D2</th><th>D3</th><th>D</th><th>D1</th><th>D2</th><th>D3</th></tr></thead><tbody><tr><td>ALD52</td><td>3.5</td><td>-10.1</td><td>5.7</td><td>-1.1</td><td>211</td><td>299</td><td>399</td><td>70.8</td><td>0.88</td><td>0.69</td><td>0.77</td><td>0.81</td></tr><tr><td>ALD52n</td><td>24.5</td><td>4.1</td><td>17.9</td><td>18.2</td><td>293</td><td>267</td><td>503</td><td>135</td><td>0.87</td><td>0.71</td><td>0.76</td><td>0.81</td></tr><tr><td>ALD63n</td><td>-0.7</td><td>10.5</td><td>15.7</td><td>-12.4</td><td>261</td><td>392</td><td>471</td><td>105</td><td>0.88</td><td>0.55</td><td>0.72</td><td>0.88</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>FIGURE 3 Mean precipitation for DJF, MAM, JJA and SON (rows) during the 1979–2012 period for (a) MESCAN with corresponding relative biases for (b) ALD52, (c) ALD52n and (d) ALD63n. DJF, december january february; MAM, march april may; JJA, june july august; SON, september october november [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_9.png" /></figure>

**FIGURE 4** Spatial Taylor diagrams of mean precipitation for (a) DJF, (b) MAM, (c) JJA and (d) SON in the 1979–2012 period over the domain as in Figure 3 for ALD52, ALD52n and ALD63n with respect to MESCAN. The diagram summarizes the standard deviation (the radial distance from the origin, dashed line), spatial pattern correlation coefficient (azimuth, dotted line) and root-mean-square difference (RMSD, the radial distance from the observations, dash-dotted line). DJF, december january february; MAM, march april may; JJA, june july august; SON, september october november [Colour figure can be viewed at wileyonlinelibrary.com]

and D2, except in autumn, with annual amplitude slightly underestimated. ALD63n displays the largest annual variability over both D1 and D2, considerably underestimating mean precipitation in summer and September and overestimating mean precipitation during the rest of the year, which is also in agreement with the previously shown maps. This follows the overestimation of the annual amplitude by a factor of 2 over D1 and 1.6 over D2. The rank correlation is largest for ALD52 and ALD52n, but nonetheless, it is still highly independent of the RCM (all CRCOs larger than 0.9).

Figure 5c shows the annual cycle within D3, which is a typical continental annual course with highest mean precipitation during the warm half of the year and lowest during the winter. CNRM-ALADIN simulations are very close to the observed mean precipitation in late autumn and winter, but in the warm part of the year, we find discrepancies. ROYA is close to 1 for ALD52, but ALD52n overestimates the annual amplitude (ROYA approximately 1.5). In contrast to the maritime type of annual courses, for continental type, ALD63n underestimates the annual amplitude. SN leads to the largest differences during late spring and summer over each domain when comparing ALD52n with ALD52. All CNRM-ALADIN simulations show very similar high CRCO values.

### 3.1.2 | Precipitation indices

To evaluate the representation of daily precipitation, we examine the wet day indices. Figure 6 summarizes the

WDF. MESCAN shows the largest WDF along the mountain ranges (40% days year⁻¹ and above), especially over the mountains of central Bosnia and Herzegovina, and the lowest over the lowlands (less than 30% days year⁻¹). ALD52 overestimates the WDF over the entire domain, and in ALD52n, the overestimation is even more pronounced (Figure 6b,c), confirming that the use of SN tends to increase precipitation (Figure S3a). ALD63n overestimates the WDF over the Gorski Kotar and Velebit but shows good performance over the Montenegrin high plains and mountains. Over the lowlands, ALD63n underestimates the WDF (Figure 6d). A new model configuration leads to a reduction in the WDF of up to 10% (30%) over the mountain ranges (lowlands and sea; Figure S3e).

The spatial Taylor diagram of the WDF fields (Figure 6h) shows that ALD52n has the closest standard deviation to MESCAN over the domain, while all simulations have a similarly high spatial correlation (just below 0.9). All CNRM-ALADIN simulations, for both D1 and D2, overestimate the standard deviation. ALD52 and ALD52n are more correlated with MESCAN than ALD63n over D2 and even more pronounced over D1. All CNRM-ALADIN simulations underestimate the standard deviation over D3, with ALD63n showing a slightly larger correlation coefficient than ALD52 and ALD52n.

The annual cycle over each subdomain (Figure 6e-g) shows very good agreement of ALD52 and ALD52n with MESCAN during autumn and winter, but for the rest of the year, they largely overestimate the WDF over all subdomains (by more than 10%). During late autumn, winter and spring, ALD63n overestimates the WDF over D1 and
<figure><img src="image_10.png" /></figure>

FIGURE 5 Annual cycle of mean precipitation during the 1979–2012 period averaged across the subdomains (a) D1, (b) D2 and (c) D3 (see Figure 1 for subdomains) with climatological rank correlation (CRCO) and the ratio of yearly amplitudes (ROYA) values for MESCAN, ALD52, ALD52n and ALD63n [Colour figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_11.png" /></figure>

FIGURE 6 Wet day frequency (WDF) spatial distribution over Dinaric Alps during the 1979–2012 period for (a) MESCAN and WDF relative bias for (b) ALD52, (c) ALD52n and (d) ALD63n. The annual cycle of WDF averaged across the subdomains (e) D1, (f) D2 and (g) D3, and (h) spatial Taylor diagram of WDF within these subdomains and the entire domain D during the 1979–2012 period for MESCAN, ALD52, ALD52n and ALD63n [Colour figure can be viewed at wileyonlinelibrary.com]

D2, but during the summer and early autumn, a substantial underestimation is present. Over D3, ALD63n is the closest to observations during winter, while showing a considerably smaller WDF during the warm part of the year. Overall, CNRM-ALADIN simulations overestimate the yearly amplitude, in some cases twice the observed amplitude (ROYA approximately 2, not shown). The distinct model configurations of ALD52n and ALD63n lead to WDF discrepancies in late spring and summer, most likely because of the different convection parameterizations. Concerning the influence of SN in the former model configuration, it leads to a substantially higher WDF during the warm part of the year.

After the WDF, we examined how many of these wet days were extremely wet days (R99p<sub>OBS</sub>; Figure 7).

The 99th percentile limit from MESCAN for the entire 1979–2012 period amounts to 35.7 mm day⁻¹ (Figure 7h), and values for each month during this period are shown in Figure 7e. The occurrence of extremely wet days in MESCAN is high over the areas of the Gorski Kotar and Velebit, inland mountains of Bosnia and Herzegovina and Montenegrin littoral mountains (Figure 7a). In all CNRM-ALADIN simulations, the same areas of high R99p<sub>OBS</sub> stand out as in MESCAN but with different spatial extents (not shown). ALD52 and ALD52n are very similar (Figures 7b,c and S3c), underestimating R99p<sub>OBS</sub> over almost the entire domain and implicating no important effect of SN application. ALD63n shows good agreement with MESCAN over D1 but mostly overestimates the R99p<sub>OBS</sub>, especially over the mountains in Bosnia and
<figure><img src="image_12.png" /><figcaption>FIGURE 7 Same as Figure 6 but for extremely wet days (R99p<sub>OBS</sub>). The 99th percentile of daily precipitation for MESCAN over the entire domain is indicated for each month above the annual cycle for the D1 and for the full period above the Taylor diagram [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

Herzegovina (Figure 7d). The new model configuration produces a larger number of extremely wet days, notably over the mountain ranges (Figure S3g).

The Spatial Taylor diagram shows a similarly high correlation coefficient over the entire domain and D1 for all CNRM-ALADIN simulations (Figure 7h). Over D2 and D3, ALD63n has a distinctly higher correlation coefficient than ALD52 and ALD52n. ALD63n overestimates the standard deviation over all domains and is closer to the observed deviation than ALD52 and ALD52n only over D1. ALD52 and ALD52n show smaller variability than MESCAN (except for D2), but regardless of the domain, their differences in spatial correlation and standard deviation are negligible.

ALD52 and ALD52n underestimate R99p<sub>OBS</sub> over D1 (Figure 7e) and D3 (Figure 7g) during the entire year, especially during the warm part of the year, and mostly during summer and autumn over D2 (Figure 7f). It follows that the overestimation of WDF that ALD52 and ALD52n show (Figure 6) cannot be attributed to overestimation of extreme events. The effect of SN in the former model configuration is more pronounced here than on the maps, especially during summer (except for D3). ALD63n simulates the annual cycle the closest to the observed cycle over all subdomains, particularly over D1, but it also overestimates R99p<sub>OBS</sub> for almost all months, especially during summer over D3.

The mean precipitation intensity on wet days is shown in Figure 8 in the form of the SDII. Along with the SDII, we analyse the precipitation amount due to extremely wet days, P99p<sub>OBS</sub> (Figure 9). The precipitation intensity in

MESCAN (Figure 8a) reaches high values over the same mountainous areas as in Figure 7a. Farther inland along the mountain ranges, the intensity decreases, mainly over the mountains in central Bosnia and Herzegovina. In parallel, more than a quarter of the total precipitation amount expressed by P99p<sub>OBS</sub> (Figure 9a) comes from extreme precipitation over the same areas that stood out with a high SDII. Again, ALD52 and ALD52n are very similar, underestimating both SDII and P99p<sub>OBS</sub> over almost the entire domain, except over the mountains in central and eastern Bosnia and Herzegovina, where ALD52 shows good agreement with MESCAN and ALD52n, with slight overestimation (Figures 8b,c and 9b,c). SN in ALD52n produces only slightly higher intensity and extreme precipitation amounts than the simulation without SN (Figure S3b,d). ALD63n captures well the patterns of a higher SDII and P99p<sub>OBS</sub> over the complex coastal mountainous areas of the Gorski Kotar and Velebit and even over the Julian Alps. Furthermore, it shows SDII (P99p<sub>OBS</sub>) values similar to those observed over the Pannonian Basin (Montenegrin littoral mountains), but it still overestimates the observed values over the mountains of central and eastern Bosnia and Herzegovina (Figures 8d and 9d). The new model configuration generates at least 20% higher precipitation intensity and at least 10% larger extreme precipitation amounts than the former configuration (Figure S3f,h).

The SDII spatial correlation coefficient for ALD63n is higher than that for ALD52 and ALD52n over D2 and only slightly over the entire domain, D1 and D3 (Figure 8h). Considering the spatial variability, all simulations show
<figure><img src="image_13.png" /><figcaption>FIGURE 8 Same as Figure 6 but for simple daily intensity index (SDII) [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>FIGURE 9 Same as Figure 7 but for the precipitation amount due to extremely wet days (P99p<sub> OBS</sub>) [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

good agreement with observations. In terms of P99p<sub> OBS</sub>, ALD52 and ALD52n have higher correlation coefficients than ALD63n over the entire domain and slightly over D1 and D2 (Figure 9h). The correlation coefficient is low over D3, approximately 0.6 for ALD63n and approximately 0.4 for ALD52 and ALD52n. The standard deviation in CNRM-ALADIN simulations is close to observations over all subdomains.

The inter-comparison of annual cycles of the SDII and P99p<sub> OBS</sub> follows a similar line of discussion as the annual cycles of R99p<sub> OBS</sub> (Figures 8e,f,g and 9e,f,g). Over D1 for

both indices and over D3 for the SDII only, ALD63n out-
performs the previous version by showing similar yearly
amplitude and time variability as MESCAN, while ALD52
and ALD52n show considerable underestimation during
the warm part of the year. Over D2 for the SDII and over
D3 for P99p<sub> OBS</sub>, ALD52n appears to be the closest to the
observations.

The circular pattern of wet bias in the northern part
of the domain in Figures 7b–d and 9b–d comes from a
similar pattern in MESCAN, and the explanation for this
behaviour is being investigated.
<figure><img src="image_15.png" /><figcaption>**FIGURE 10** Probability distribution function (PDF) of daily precipitation intensity during the 1979–2012 period within the subdomains (a) D1, (b) D2 and (c) D3 (see Figure 1 for subdomains) for MESCAN, ALD52, ALD52n and ALD63n [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

### 3.1.3 | Probability distribution function and Q-Q plots

Figure 10 shows the PDF of daily precipitation intensity for the three subdomains. All CNRM-ALADIN simulations, densely spaced around MESCAN values, show the capability of producing, more or less successfully, the frequency of medium- and high-intensity events. To help with interpretation, we also analyse the relative difference of PDFs with respect to MESCAN (Figure S4). Over all subdomains, there is a turning point between the intensity of 10 and 20 mm day⁻¹, below which ALD52 and ALD52n show overestimation and ALD63n, but to a lesser extent, shows an underestimation of precipitation frequency. For D1, ALD52 and ALD52n tend to further underestimate the frequency of medium- and high-intensity events (Figures 10a and S4a). The medium events are overestimated by ALD63n, but those of high intensity are mostly underestimated. A similar situation can be found for D2, except that medium intensity events are also overestimated by ADL52 and ALD52n (Figures 10b and S4b). However, the robustness of this relationship between CNRM-ALADIN and MESCAN over D2 is limited due to the low station density and documented underestimations in MESCAN found over the mountainous regions in Isotta et al. (2014b). Within D3 (Figures 10c and S4c), the ALD52 frequency deteriorates towards higher

precipitation intensity, displaying a large underestimation, while in contrast, ALD52n appears to be the closest to MESCAN, and ALD63n overestimates the occurrence of medium- and high-intensity events. Spectrally nudged simulations produce a higher frequency regardless of the precipitation intensity or subdomain.

Figure 11 shows quantile-quantile plots with a 0.1 mm day⁻¹ step of daily precipitation for 12 stations (Figure 1) with various altitudes and distances from the sea. Although almost all stations, except Crkvice and Herceg Novi, are used to build MESCAN, MESCAN shows good agreement with observations for only half of these stations (Dubrovnik, Gospić, Ogulin, Osijek and Zagreb). For the rest, it underestimates precipitation, mainly extreme precipitation (above 99th quantile; Rijeka, Sarajevo, Zadar and Zavižan), or in one case, overestimates precipitation over almost the entire quantile range (Split).

CNRM-ALADIN simulations remain fairly close to each other and observations (except for Sarajevo and Zavižan) for the medium-intensity precipitation (95th–99th quantile). However, on closer inspection, ALD63n is the closest to observations for most stations, followed by ALD52n and ALD52. High- and extreme-intensity precipitation (above the 99th quantile) is underestimated by all simulations for almost all stations. Here, ALD63n shows smaller discrepancies for a slightly larger number of stations than ALD52 and ALD52n. Over the
<figure><img src="image_16.png" /><figcaption>FIGURE 11 Quantile-quantile plots of daily precipitation during the 1979–2012 period for meteorological stations in Figure 1: (a) Crkvice (Montenegro; MN), (b) Dubrovnik (Croatia; CR), (c) Gospić (CR), (d) Herceg Novi (MN), (e) Ogulin (CR), (f) Osijek (CR), (g) Rijeka (CR), (h) Sarajevo (Bosnia and Herzegovina), (i) Split (CR), (j) Zadar (CR), (k) Zagreb (CR) and (l) Zavižan (CR). The station altitudes and corresponding subdomains are indicated. Observed quantiles are placed along the x-axis and quantiles for MESCAN, ALD52, ALD52n and ALD63n along the y-axis. Observed 95th (dashed line) and 99th quantile (dotted line) are indicated both based on the whole time series. Each subfigure contains a small graph representing the lowest quantiles (up to 10 mm day⁻¹; upper left corner) [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

entire precipitation range, regardless of the station, SN in ALD52n constantly produces a somewhat higher precipitation intensity than ALD52 simulation.

The stations not included in MESCAN, Crkvice and Herceg Novi, show the highest degree of underestimation compared with the rest of the stations, not only by all CNRM-ALADIN simulations but also by MESCAN. For Herceg Novi, the station on the coast and at the foot of the

Montenegrin Orjen Mountain, precipitation intensities are generally underestimated, with the highest intensities at approximately 50% of the observed values. Station Crkvice, located at the slope of Orjen Mountain, holds the record for the highest average annual precipitation in Europe (Ducić et al., 2012). The 99th quantile for Crkvice is higher than 170 mm day⁻¹, and the highest intensity is just below 350 mm day⁻¹. All CNRM-ALADIN simulations
<figure><img src="image_17.png" /><figcaption>FIGURE 12 Daily precipitation for IOP2 (September 12, 2012) accumulated for 24 hr on rain gauges (circles) and (a) MESCAN, (b) ALD52, (c) ALD52n, (d) ALD63n and sensitivity test (e) ALD63n-t1, (f) ALD63n-t2 and (g) ALD63n-t3 precipitation [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

and MESCAN substantially underestimate the medium-
and extreme-intensity precipitation.

The graphs in the upper left corner of each subfigure show low-intensity precipitation (less than 10 mm day⁻¹). For almost all stations, MESCAN and CNRM-ALADIN simulations show the overestimation of light precipitation, especially when observations show zero precipitation. The too-frequent production of low-intensity precipitation, or the so-called drizzle problem, is a known issue with not only RCMs (Boberg et al., 2010; Kjellström et al., 2010; Kendon et al., 2012) but also re-analysis (e.g., Isotta et al., 2014b). This is also noticeable in Figure 10 for the daily precipitation intensity PDF. ALD52n shows the strongest overestimation, but the new model configuration in ALD63n improved upon this issue, showing the smallest discrepancies and very good agreement with observations for several stations.

3.2 | The evaluation of the HyMeX IOPs

3.2.1 | The extreme daily precipitation
during IOP2

Among the IOPs that strongly affected the Adriatic coast
and littoral mountainous area during HyMeX SOP1, we
specifically examine IOP2, which was the most extreme
HPE. Namely, during IOP2, substantial amounts of pre-
cipitation fell over several hours over the coastal area

surrounding the City of Rijeka and its mountainous hinter-
land (Gorski Kotar) in the northern Adriatic Sea. The high-
est precipitation recorded during this HPE was 220 mm
day⁻¹ at meteorological station Rijeka, and consequently,
the intensity of this HPE caused severe flooding and
damage. The synoptic and mesoscale situation was thor-
oughly described in Ferretti *et al.* (2014) and Ivančan-Picek
*et al.* (2016).

Figure 12 shows the precipitation on the first day of IOP2, and in addition to MESCAN, simulations are evaluated against rain gauge observations. MESCAN illustrates the aforementioned situation with the maximum precipitation amount over Gorski Kotar and coastal areas near the city of Rijeka. The rain gauge observations indicate that precipitation is more localized than in MESCAN, especially over Gorski Kotar. ALD52 appears to completely miss the event, and ALD52n shows some improvement due to SN but still largely underestimates the precipitation amount and spatial extent of this HPE. In contrast, ALD63n represents the event with a rather good spatial distribution, although the precipitation pattern over Gorski Kotar is not as broad as in MESCAN. It also shows very high values over the Velebit mountain range which are not observed. All sensitivity tests show a similar spatial pattern with three maxima. In ALD63n-t1, with the increase in entrainment and aerodynamical drag, precipitation decreases over Gorski Kotar and increases over the sea. In ALD63n-t2, with the added turbulence modifications, the effect of the ALD63n-t1 sensitivity test lessens,
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>FIG URE 13 SAL diagrams for the daily precipitation from (a) ALD52, (b) ALD52n, (c) ALD63n and sensitivity test (d) ALD63n-t1, (e) ALD63n-t2 and (f) ALD63n-t3 against MESCAN for every day of each IOP. Different symbols for each IOP show the values of all three components of SAL. The number in the symbol indicates the day of IOP (for example, the square with number 2 is showing SAL for IOP19 on November 5). The day of the IOP with maximum precipitation is highlighted with thicker symbol lines. Dashed lines represent median values of S and A components, and the grey rectangle indicates the 25th and 75th percentile of the S and A distribution. The colour of the symbol represents the L component (see the colour bar in the top left of (b)) [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

and the precipitation increase is displayed compared with the control run. In ALD63n-t3, with the dynamics modifications, the spatial distribution of the previous sensitivity test is not altered, but higher precipitation values are shown, both over Gorski Kotar and Velebit.

Precipitation for the rest of the selected IOPs is shown in Figures S5–S9, and almost all CNRM-ALADIN simulations capture the occurrence of HPEs. This ability of RCMs to capture HPEs when forced with re-analysis has been confirmed in previous studies (Berthou *et al.*, 2015; Khodayar *et al.*, 2016). Moreover, the application of SN brings more consistency to large-scale circulation, which improves the results in most cases. However, precipitation is more sensitive to the model configuration change than to the application of SN. This suggests that the new physical parameterizations, especially convection, turbulence (prognostic TKE with a TKE-based mixing length, compared with diagnostic TKE with a bulk-mixing length) and microphysics (prognostic water content compared with

diagnostic ones), could play an important role in the representation of the precipitation field in the majority of IOPs.

### 3.2.2 | SAL method

Figure 13 shows the SAL results in diagrams where the daily precipitation from CNRM-ALADIN simulations is compared with MESCAN for every day (number inside symbol) of each IOP (different symbols). SAL results for the first day of IOP2 (circle with number 1) correspond to Figure 12. ALD52n misses the IOP2 event by underestimating the precipitation amount with a smaller precipitation object than that observed. ALD52n shows improvement in all SAL components, especially in L and S, but still underestimated the amount. ALD63n sensitivity tests have very similar SAL values, and the A and L components are close to zero but with too large and/or flat precipitation objects.
Overall, the median of the S component for ALD52 is positive (approximately 0.4), indicating that, in most cases, ALD52 creates objects that are too large and/or too flat, while the A component shows an almost equal number of events for which ALD52 overestimates or underestimates precipitation. The SN improves the L component, and the A component shows smaller variability. Additionally, ALD52n shows most S and A values in the first quadrant, indicating that the simulations mainly overestimate precipitation by developing objects that are too large and/or too flat. At the event scale, the SN leads to an overestimation of daily precipitation (positive median of the A component), as already found in the climatological analysis (Section 3.1).

ALD63n displays most S and A values in the first and third quadrants. The SAL results for the sensitivity tests are very similar, showing subtle differences. All tests show at least twice the number of positive than negative A values, that is, overestimating the domain-averaged total precipitation in the majority of cases. Considering only the L component, ALD63n-t1 shows the best results, that is, outnumbering the rest of the tests with the smallest L values. The S component seems to be the most robust. Only ALD63n-t1, with increased entrainment and aerodynamical drag, shows a negative median of S, indicating that the precipitation objects become smaller and/or more peaked. However, S becomes positive in ALD63n-t2, with increased turbulence in the presence of convection, and in ALD63n-t3, with added dynamics modifications. The RCM's tendency to overestimate the S component has been found in the study of IOP12, and it is attributed to their relatively coarse resolution when compared with NWP and convection-permitting models (Khodayar et al., 2016). It is worth noting the distribution of S and A components, which is better for ALD63n-t3 (symmetry along with A and S with the lowest variance) than for the rest of the sensitivity tests (asymmetry along with A for ALD63n and ALD63n-t2 and along S for ALD63n-t1).

Finally, there is no clear conclusion about which CNRM-ALADIN version or which ALD63n sensitivity test outperforms others. ALD52n shows the smallest variability of S and A components, while ALD63n shows the median of S and A components closest to zero, but ALD63n-t1 shows the best L values. However, the use of SN clearly shows improvement in SAL values.

### 3.2.3 | Atmospheric stratification and instability

We investigate in more detail why ALD63n sensitivity tests capture some of the IOPs better than others examining the physical processes at the subdaily scale. The analysis

is limited to the day of the IOP with maximum precipitation, that is, 6 days (Table 3). Based on the SAL results, the analysis is divided into two parts. First, we analyse the IOPs for which both the S and A components are larger than 0.5 for almost all sensitivity tests. On the day with maximum precipitation during IOP13 (second day), IOP18 (first day) and IOP19 (first day), all sensitivity tests overestimate the domain-averaged precipitation amount with precipitation objects that are too large and/or too flat (Figure 13). The sensitivity test temperature profiles are very similar, and they are close to the observed profiles in the middle and upper levels (Figure 14d-f). In the lowest layers, observations show temperature inversion, which ALD63n sensitivity tests capture, although with overestimation of temperature and inversion height. This is an encouraging result that appears to be related to the TKE scheme used in ALD63n, which is able to represent non-local mixing effects in the planetary boundary layer. Sensitivity tests overestimate the amount of moisture at the majority of levels, especially at the lowest layers where IOP13 and IOP19 reach saturation, which is not observed. Sensitivity tests are not able to reproduce sharp changes in humidity profiles, particularly the dry layers for IOP18 and IOP19. This overestimation of temperature and humidity at the lowest levels probably led to the overestimation of domain-averaged precipitation noted in the SAL diagrams. The observed CAPE and CAPE3 are zero, and the LI and K-index (except for IOP13) show no potential for convective storms (Figure 15a,b). For IOP18 and IOP19, all ALD63n sensitivity tests agree well with observations for CAPE, CAPE3, CIN and LI. Observations for IOP18 and IOP19 show that wind shear increases with layer height, and all ALD63n sensitivity tests produce stronger shear than observed, particularly for IOP19 (Figure 15c). For IOP13, the sensitivity tests indicate the possibility of severe weather, probably due to the overestimation of moisture in the lowest layers, which directly impacts CAPE/CAPE3.

Second, we focus on the IOPs for which sensitivity tests perform rather well based on SAL results. On the day with maximum precipitation, most of the sensitivity tests very slightly overestimate the domain-averaged precipitation amount ($A \le 0.1$) with larger or flatter precipitation objects for IOP2 (first day) and IOP16 (second day) and with smaller or more peaked precipitation objects than observed for IOP4 (first day). The observed vertical profiles for IOP4 and IOP16 show a high level of moisture throughout the entire column and reach saturation at some point at low (IOP16; Figure 14c) or middle (IOP4; Figure 14b) levels. The observed CAPE and CAPE3 values for these IOPs are very high, and the stability indices (LI and K-index) indicate an unstable environment. For IOP4, all ALD63n sensitivity tests agree very well with the observed vertical profiles, except in the lowest layer
<figure><img src="image_20.png" /><figcaption>FIGURE 14 Skew-T log-p diagrams of atmospheric temperature (solid line) and humidity (dashed line) profiles for ALD63n and sensitivity tests (ALD63n-t1, ALD63n-t2 and ALD63n-t3) and radiosounding observations at station Zadar-Zemunik for (a) IOP2, (b) IOP4, (c) IOP16, (d) IOP13, (e) IOP18 and (f) IOP19 [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

(below 700 hPa), where the temperature is underestimated and saturation is already reached for some tests, which reflects the underestimation of CAPE and overestimation of CAPE3 values. The exception is ALD63n-t1, with the driest conditions in the lowest layer and zero CAPE3. For IOP16, sensitivity tests are drier and colder than the observations, especially in the middle and upper levels. Again, ALD63n-t1 is an exception among the sensitivity tests, with the driest conditions in the lowest layer. Most likely, these conditions caused a decrease in S and L components for IOP16, both very close to zero (Figure 13d). The

CAPE3 values for IOP16 are underestimated by all sensitivity tests, especially by ALD63n-t1. ALD63n-t1 is the only test that underestimates CAPE values, while the control run shows a very accurate amount and other tests show overestimation. For both IOP4 and IOP16, the LI and K-index are very close to the observed values. For IOP2 (Figure 14a), sensitivity tests overestimate temperature in the first 800 hPa and produce somewhat drier conditions below 900 hPa, but above that level, the amount of moisture is overestimated, thus leading to CAPE and CAPE3 overestimation. The wind shear for IOP2 is less
<figure><img src="image_21.png" /><figcaption>FIGURE 15 (a) Full CAPE values, CAPE integrated up to 3 km height (CAPE3) and CIN, (b) the lifted index (LI) and K-index, (c) the 0–1, 0–3 and 0–6 km bulk shear for ALD63n and sensitivity tests (ALD63n-t1, ALD63n-t2 and ALD63n-t3) and radiosounding observations at station Zadar-Zemunik for IOP2, IOP4, IOP16, IOP13, IOP18 and IOP19 [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

than 10 m·s⁻¹ in all layers, and ALD63n sensitivity tests show a slight overestimation.

Overall, this analysis of physical processes that preceded HPEs has several limitations. First, we use information regarding the vertical structure at a single location at a specific time to examine very local conditions, and the risk of large differences between the model and observations is high because of distinct spatial and temporal variability. Second, the location is not the most

representative of preconvective conditions for all IOPs (see subsection 2.3.2), and third, the analysis is performed for only 1 day of the IOP, the day of maximum convective activity. Nevertheless, CNRM-ALADIN shows the ability to realistically simulate atmospheric stratification considering both the horizontal and vertical resolutions common for an RCM, although it shows discrepancies, especially with humidity profiles. Khodayar et al. (2016) had similar results for vertical profiles during IOP12, and they
<figure><img src="image_22.png" /></figure>

pointed out that these inconsistencies in vertical moisture distribution, together with triggering mechanisms of deep convection, were the most likely reason why models with parameterized convection underestimate the precipitation amounts and intensity during HPE. The intra-test comparison shows that all sensitivity tests are very similar and that no test outperforms the others. This demonstrates that the limited spread of ALD63n results in specific parameter changes in the physical parameterization schemes. To better understand why ALD63n sensitivity tests capture some IOPs better than others, in addition to the discrepancies in vertical moisture and temperature distribution, further investigation is required in the scope of physical processes dedicated study.

4 | CONCLUSIONS

One of the key modelling challenges for the regional climate model (RCM) community is the correct representation of precipitation, especially heavy precipitation events (HPEs). In the performance of RCMs, one of the largest sources of errors and uncertainties is the physical parameterizations of unresolved subgrid processes. Thus, we examine the sensitivity of extreme daily precipitation in an RCM (CNRM-ALADIN) to different model physics configurations at climatological and event (heavy precipitation events, HPEs) temporal scales, as well as the impact of the spectral nudging (SN) technique used to control the model large scales. Additionally, at the event scale, we perform several tests to investigate the sensitivity of extreme precipitation to specific parameters in the new physical parameterization schemes of the latest CNRM-ALADIN version. The simulations are driven by ERA-Interim over the Med-CORDEX domain at a 0.11° spatial resolution. The study domain encompasses the topographically complex coastal mountainous area of the Dinaric Alps and eastern Adriatic, which is often affected by HPEs.

The model physics configuration has a substantial impact on the representation of mean and extreme daily precipitation at all examined time scales. At the climatological scale, the latest model configuration with new physical parameterizations of moist processes, particularly convection, microphysics and turbulence, shows considerable differences with respect to the former configuration, especially during summer when convection is more dominant than large-scale processes. The former model configuration better represents the spatial patterns and annual cycles of mean precipitation. However, the new model physics configuration first demonstrates the improvement in the representation of precipitation intensity over the entire study domain and especially over the complex coastal mountainous area in the north-eastern

Adriatic. Second, substantial improvement is also found for light precipitation, which the former configuration, especially with the application of SN, simulates too frequently. This indicates that the recently developed version of CNRM-ALADIN improves the wet day statistics and thus partly solves the “drizzle” problem, a common issue identified in many RCMs so far (Boberg et al., 2010; Kjellström et al., 2010; Kendon et al., 2012; Fantini et al., 2018), and not entirely resolvable with the increase in resolution of convection parameterized RCMs (Fantini et al., 2018). Third, the latest model physics configuration also shows improvement in extreme precipitation representation over the complex coastal mountainous area of the northern and southern Dinaric Alps and even over the Julian Alps. Nevertheless, there is scope for further improvement due to the still-present underestimation of the highest quantiles.

At the event time scale, extreme precipitation shows to be considerably sensitive to changes in the model physics configuration in almost every examined aspect: occurrence, spatial variability, domain-averaged precipitation, precipitation structure and location of HPEs. For some of these aspects, there is no clear answer as to which version is superior, except for the occurrence of HPEs, their spatial variability and the structure of precipitation objects. The former model configuration shows a clear tendency to produce precipitation objects that are too large or too flat, which is consistent with a previous study of a single HPE by Khodayar et al. (2016). They attributed this deficiency to the model’s coarse resolution. However, the new model configuration lessens this tendency towards too large precipitation objects with an improvement in physical parameterizations without an increase in the spatial resolution. Furthermore, it shows improvement in capturing the occurrence and spatial pattern of most HPEs.

The use of SN brings a limited added value for the representation of daily precipitation over the study area. The SN simulations show more consistency with large-scale circulation by improving temporal variability at the climatological scale and correcting the location of the HPEs at the event scale. Moreover, the simulation with SN shows slightly higher values of precipitation intensities and extreme precipitation than the simulations without SN, both simulations showing underestimation. However, SN contributes to a larger wet bias of mean precipitation and higher frequencies of already highly overestimated light precipitation in the former model configuration. All of these differences are enhanced during the warm part of the year. The SN modifies the large scale circulation, that is, change in the cyclone tracks; however, further analysis is required to investigate why the SN impacts the precipitation in this way. Overall, extreme precipitation shows a higher degree of sensitivity to changes in
model configuration, particularly physical parameteriza-
tions, than to the application of SN.

The sensitivity of daily extreme precipitation to specific
parameters in physical parameterization schemes in the
new model physics configuration is highly event depen-
dent. Overall, the location of precipitation objects shows
slightly higher sensitivity than domain-averaged precipita-
tion and precipitation structure. At the subdaily scale, the
sensitivity of the vertical distribution of moisture is more
distinct than that of the temperature, especially in the
lower and middle levels of the troposphere. In these layers,
the moisture profiles also show the largest discrepancies
when compared with radiosounding measurements, and
precisely, these inconsistencies, together with the ability to
trigger deep convection, are suspected to be the reason why
models with parameterized convection underestimate pre-
cipitation intensity during an HPE (Khodayar et al., 2016).
Accordingly, the stability indices highly dependent on
moisture profiles are also somewhat sensitive to parame-
ter changes, unlike the vertical wind shear estimated for
several different layers.

Conclusively, this study shows that investing time and
effort in the development and advancement of physical
parameterizations in RCMs can improve the representa-
tion of extreme precipitation at several time scales and
therefore reduce uncertainties and contribute to build-
ing confidence in future climate projections. Nevertheless,
there are several limitations to our study. The conclusions
may suffer from a relatively low station density in our ref-
erence dataset, and they are limited to the study area, only
one RCM and a relatively small number of selected HPEs.
Further research should address the same problem using
a larger number of RCMs. Likewise, it would be interest-
ing to include convection-permitting RCMs, which show
added value with respect to RCMs with parameterized con-
vection, especially when tackling extreme precipitation at
subdaily scales (Chan et al., 2013; Ban et al., 2014; Berthou
et al., 2018; Fumière et al., 2020; Caillaud et al., 2021). The
latter would be especially interesting for studying the rep-
resentation of HPEs. Another priority is the development
of a high-resolution observational dataset over the broader
Dinaric Alps region.

ACKNOWLEDGEMENTS

We acknowledge the data providers in the ECA&D
project, data and metadata available at https://www.
ecad.eu. We thank the ECMWF for the SYNOP and
climatological station data. This work included the
UERRA (EU FP7 Collaborative Project, grant agreement
607193) project MESCAN-SURFEX data (http://www.
uerra.eu/component/dpattachments/?task=attachment.
download&id=399). CNRM-ALADIN simulations and
MESCAN were processed with the help of the Climate

Data Operators (CDO; https://code.mpimet.mpg.de/projects/cdo; Schulzweida, 2019). Alex Deckmyn from Royal Meteorological Institute of Belgium is appreciated for providing us with the SAL code of the Hirlam-Aladin R Package for verification toolbox (HARP). This work is a contribution to the HyMeX programme (www.hymex.org) and to the Med-CORDEX initiative (www.medcordex.eu). The CNRM-ALADIN version 5.2 and CNRM-ALADIN version 5.2 with spectral nudging simulations can be downloaded from the Med-CORDEX database. The CNRM-ALADIN version 6.3 test simulations were performed using Météo-France HPC, and we thank Météo-France, CNRM for providing the resources. We also thank Dragan Burić of the Department of Geography, the University of Montenegro for providing the Crkvice and Herceg Novi daily precipitation data. The contribution of Sarah Ivušić to this article was partially funded by HrZZ (Croatian Science Foundation) contract I-3833-2016 and by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 653824/EU-CIRCLE. The authors would like to thank the reviewers for their suggestions and comments, which have greatly improved this article.

AUTHOR CONTRIBUTIONS

**Sarah Ivušić:** Conceptualization; data curation; formal analysis; investigation; methodology; software; visualization; writing – original draft; writing – review and editing.

**Ivan Güttler:** Conceptualization; data curation; investigation; methodology; supervision; writing – original draft; writing – review and editing. **S Somot:** Conceptualization; data curation; methodology; supervision; validation; writing – original draft; writing – review and editing. **Jean-François Gueremy:** Conceptualization; data curation; methodology; writing – original draft; writing – review and editing. **Kristian Horvath:** Conceptualization; funding acquisition; project administration; supervision; writing – original draft. **Antoinette Alias:** Data curation; methodology; writing – original draft.

ORCID

Sarah Ivušić https://orcid.org/0000-0001-9068-7130

Ivan Güttler https://orcid.org/0000-0002-9189-5510

Jean-François Guérémy https://orcid.org/0000-0002-2981-4839

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## SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Ivušić, S., Güttler, I., Somot, S., Guérémy, J.-F., Horvath, K. & Alias, A. (2021) Modelling extreme precipitation over the Dinaric Alps: An evaluation of the CNRM-ALADIN regional climate model. *Quarterly Journal of the Royal Meteorological Society*, 147(741), 4425–4453. Available from: https://doi.org/10.1002/qj.4187