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Heavy precipitation is a challenging phenomenon with high impact on human lives and infrastructure, and thus a better modelling of its characteristics can improve understanding and simulation at climate timescales. The achievement of convection-permitting modelling (CPM) resolutions (Δx km) has brought relevant advancements in its representation. However, further research is needed on how the very high resolution and switching-off of the convection parameterization affects the representation of processes related to heavy precipitation. In this study, we evaluate reanalysis-driven simulations for the greater Alpine area over the period 2000–2015 and assess the differences in representing heavy precipitation and other model variables in a CPM setup with a grid size of 3 km and a regional climate model (RCM) setup at 25 km resolution using the COSMO-CLM model. We validate our simulations against high-resolution observations (E-OBS (ENSEMBLES observations), HYRAS (Hydrometeorologische Rasterdatensätze), MSWEP (Multi-Source Weighted-Ensemble Precipitation), and UWYO (University of Wyoming)). The study presents a revisited version of the precipitation severity index (PSI) for severe event detection, which is a useful method to detect severe events and is flexible for prioritizing long-lasting events and episodes affecting typically drier areas. Furthermore, we use principal component analysis (PCA) to obtain the main modes of heavy precipitation variance and the associated synoptic weather types (WTs). The PCA showed that four WTs suffice to explain the synoptic situations associated with heavy precipitation in winter, due to stationary fronts and zonal flow regimes. Whereas in summer, five WTs are needed to classify the majority of heavy precipitation events. They are associated with upper-level elongated troughs over western Europe, sometimes evolving into cutoff lows, or with winter-like situations of strong zonal circulation. The results indicate that CPM represents higher precipitation intensities, better rank correlation, better hit rates for extremes detection, and an improved representation of heavy precipitation amount and structure for selected events compared to RCM. However, CPM overestimates grid point precipitation rates, which agrees with findings in past literature. CPM systematically represents more precipitation at the mountain tops. However, the RCMs may show large intensities in other regions. Integrated water vapour and equivalent potential temperature at 850 hPa are systematically larger in RCM compared to CPM in heavy precipitation situations (up to 2 mm and 3 K, respectively) due to wetter mid-level conditions and an intensified latent heat flux over the sea. At the ground level, CPM emits more latent heat than RCM over land (15 W m−2), bringing larger specific humidity north of the Alps (1 g kg−1) and higher CAPE (convective available potential energy) values (100 J kg−1). RCM, on the contrary simulates a wetter surface level over Italy and the Mediterranean Sea. Surface temperatures in RCM are up to 2 ∘C higher in RCM than in CPM. This causes outgoing longwave radiation to be larger in RCM compared to CPM over those areas (10 W m−2). Our analysis emphasizes the improvements of CPM for heavy precipitation modelling and highlights the differences against RCM that should be considered when using COSMO-CLM climate simulations.

Gaining insight into the interaction between atmospheric moisture and convection is determinant for improving the model representation of heavy precipitation, a weather phenomenon that causes casualties and monetary losses in the western Mediterranean region every year. Given the large variability of atmospheric moisture, an accurate representation of its distribution is expected to reduce the errors related to the representation of moist convective processes. In this study, we use a diagnostic approach to assess the sensitivity of convective precipitation and underlying mechanisms during a heavy precipitation event (Hydrological cycle in the Mediterranean eXperiment Intensive Observation Period; HyMeX IOP16) to variations of the atmospheric moisture spatio-temporal distribution. Sensitivity experiments are carried out by nudging a homogenized data set of the Global Positioning System-derived zenith total delay (GPS-ZTD) with sub-hourly temporal resolution (10 min) in 7 and 2.8 km simulations with the COnsortium for Small-scale MOdeling in CLimate Mode (COSMO-CLM) model over the western Mediterranean region. The analysis shows that (a) large atmospheric moisture amounts (integrated water vapour; IWV ∼ 40 mm) precede heavy precipitation in the affected areas. This occurs 12 h prior to initiation over southern France and 4 h over Sardinia, north-eastern Italy and Corsica, which is our main study area. (b) We found that the moisture is swept from the Atlantic by a westerly large-scale front associated with an upper level low on the one hand and evaporated from the Mediterranean Sea and north Africa on the other. The latter moisture transport occurs in the 1 to 4 km layer. (c) COSMO-CLM overestimated the atmospheric humidity over the study region (Corsica), and this was, to a good extent, corrected by the GPS-ZTD nudging. This reduced maximum precipitation (−49 % for 7 km and −16 % for 2.8 km) drastically, considerably improving the precipitation representation in the 7 km simulation. The convection-permitting simulation (2.8 km) without the GPS-ZTD nudging already did a good job in representing the precipitation amount. (d) The two processes that exerted the largest control on precipitation reduction were the decrease of atmospheric instability over Corsica (convective available potential energy; CAPE −35 %) and the drying of the lower free troposphere bringing additional dry air entrainment. In addition, the 7 km simulation showed a weakening of the represented low-pressure system and the associated cyclonic wind circulation. This ultimately reduced the intensity and number of convective updrafts represented over the island. These results highlight the large impact exerted by moisture corrections on precipitating convection and the chain of processes leading to it across scales.

Improving the understanding and representation of heavy precipitation is crucial to prevent its hazards. A powerful means to reduce errors is to assimilate high-resolution humidity GPS observations. Here, novel experiments employing sub-hourly atmospheric GPS, in-situ observations and nudging are used to study the impact of moisture corrections on convection. This work adds new explanations on the sensitivity of extreme precipitation to moisture changes and on the added value of nudging GPS.