## 1 Introduction  
Mountain environments are being particularly affected by climate change, especially in terms of water resources and streamflow regimes [1]. In recent times, among water-related geomorphological processes often responsible of alpine landscape evolution [2], mass-wasting events are potentially triggered by climate change, and by extreme events, in particular, that could act at different time scales [3, 4]. According to [PERSON] and [PERSON] [5], the relationship between type, spatial extent, and magnitude of the changes, as well as the location, activity, and frequency of landslides in response to the projected climate change, are not straightforward. Moreover, the analysis of climate-landslide relation is complicated by the partial overlap on spatial and temporal scales between these two elements [5]. In addition, in the last decades and in the near future, due to the increasing occurrence of heavy and concentrated or prolonged rainfalls [6], mass movements, in particular debris flows, are expected to increase in frequency and/or magnitude [7; 8; 9]. The involved meteorological parameters should be investigated and forecasted with specific detail and could reveal to be effective in mitigating risk scenarios deriving from this kind of processes. In this context, the forecasts of meteorological events that trigger this kind of landslide are challenging for two main reasons. Firstly, mountain topography is complex being characterised by high peaks and sometimes narrow valleys that are hardly resolved by orographic input dataset [10]. In order to reach a high-performance model, it is necessary to decrease the vertical and horizontal grid-spacing to fit both the atmospheric physical processes and the narrow valley spaces [10]. For these reasons, the actual mesoscale meteorological models need a high-resolution input dataset for orography, land-cover, soil texture, and vegetation patterns to correctly depict the underlying terrain [11]. These detailed data are necessary as much as the boundary meteorological conditions. Secondly, the meteorological events that can trigger debris flows are characterised by continuous and prolonged rainfall or by intense precipitation in a short period over small geographical areas [12]. Especially in this second case, meteorological forecast models are hardly able to identify the amount of rainfall, their timing, and the limited areas affected by rain. On the Alps, where complex terrains make the use of high spatial resolutions mandatory in meteorological models, the grid-spacing in meteorological model applications is generally set at a value ranging between 1.0 and 1.5 km, but sometimes it might be not enough [13]. COSMO (COnsortium for Small scale MOdelling) model, operatively used by MeteoSwiss and several universities and environmental agencies for weather forecasts and research purposes, runs at the maximum resolution of 1.1 km over the Alpine arc [14; 15], while MOLOCH model, developed at ISAC-CNR (Institute for Atmospheric and Climate Sciences, National Research Council Italy) and covering all Italian peninsula, has been set at the minimum grid-spacing of 1 km [16]. RAMS (Regional Atmospheric Modelling System) has been applied over highly complex terrain, in the Hindu-Kush Karakorum and Himalaya regions, at the maximum resolution of 1 km [17]. The WRF (Weather Research and Forecasting) model [18] is widely used worldwide as a consequence of its open-source distribution, however its default set-up permits the maximum horizontal grid-spacing of 1 km over complex terrains [19; 20]. An increased horizontal and vertical resolution could provide useful information about the meteorological processes over complex terrain. All meteorological models compute the precipitation patterns and their time evolution during the event. These data are fundamental to investigate the meteorological phenomena. Moreover, they can also be used to drive hydrological model systems that can predict the amount of water resources and their fluctuations [21; 22].