The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

High‐resolution monthly precipitation climatologies for Italy are presented. They are based on 1961–1990 precipitation normals obtained from a quality‐controlled dataset of 6134 stations covering the Italian territory and part of the Northern neighbouring regions. The climatologies are computed by means of two interpolation methods modelling the precipitation‐elevation relationship at a local level, more precisely a local weighted linear regression (LWLR) and a local regression kriging (RK) are performed. For both methods, local optimisations are also applied in order to improve model performance. Model results are compared with those provided by two other widely used interpolation methods which do not consider elevation in modelling precipitation distribution: ordinary kriging and inverse distance weighting. Even though all the four models produce quite reasonable results, LWLR and RK show the best agreement with the observed station normals and leave‐one‐out‐estimated mean absolute errors ranging from 5.1 mm (July) to 11 mm (November) for both models. Their better performances are even clearer when specific clusters of stations (e.g. high‐elevation sites) are considered. Even though LWLR and RK provide very similar results both at station and at grid point level, they show some peculiar features. In particular, LWLR is found to have a better extrapolation ability at high‐elevation sites when data density is high enough, while RK is more robust in performing extrapolation over areas with complex orography and scarce data coverage, where LWLR may provide unrealistic precipitation values. However, by means of prediction intervals, LWLR provides a more straightforward approach to quantify the model uncertainty at any point of the study domain, which helps to identify the areas mainly affected by model instability. LWLR and RK high‐resolution climatologies exhibit a very heterogeneous and seasonal‐dependent precipitation distribution throughout the domain and allow to identify the main climatic zones of Italy.

A high-resolution gridded dataset of daily mean temperature and precipitation series spanning the period 1980–2018 was built for Trentino-South Tyrol, a mountainous region in north-eastern Italy, starting from an archive of observation series from more than 200 meteorological stations and covering the regional domain and surrounding countries. The original station data underwent a processing chain including quality and consistency checks, homogeneity tests, with the homogenization of the most relevant breaks in the series, and a filling procedure of daily gaps aiming at maximizing the data availability. Using the processed database, an anomaly-based interpolation scheme was applied to project the daily station observations of mean temperature and precipitation onto a regular grid of 250 m × 250 m resolution. The accuracy of the resulting dataset was evaluated by leave-one-out station cross-validation. Averaged over all sites, interpolated daily temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.5 ^∘C and 1.1 mm and a mean correlation of 0.97 and 0.91, respectively. The obtained daily fields were used to discuss the spatial representation of selected past events and the distribution of the main climatological features over the region, which shows the role of the mountainous terrain in defining the temperature and precipitation gradients. In addition, the suitability of the dataset to be combined with other high-resolution products was evaluated through a comparison of the gridded observations with snow-cover maps from remote sensing observations. The presented dataset provides an accurate insight into the spatio-temporal distribution of temperature and precipitation over the mountainous terrain of Trentino-South Tyrol and a valuable support for local and regional applications of climate variability and change. The dataset is publicly available at https://doi.org/10.1594/PANGAEA.924502 (Crespi et al., 2020).

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at a regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term “triggering” precipitation, medium-term “preparatory” precipitation, seasonal effects, and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false-alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design, and model transparency are crucial for landslide prediction using advanced data-driven techniques.

Snow is particularly impacted by climate change and therefore there is an urgent need to understand the temporal and spatial variability of depth of snowfall (HN) trends. However, the analysis of historical HN observations on large‐scale areas is often impeded by lack of continuous long‐term time series availability. This study investigates HN trends using observed time series spanning the period 1920–2020 from 46 sites in the Alps at different elevations. To discern patterns and variations in HN over the years, our analysis focuses also on key parameters such as precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, that is, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Atlantic Multidecadal Oscillation (AMO) indices. Our findings reveal that in the last 100 years and below 2000 m a.s.l., despite a slight increase in winter precipitation, there was a decrease in HN over the Alps, especially for southern and low‐elevation sites. The South‐West and South‐East regions experienced an average loss of 4.9 and 3.8%/decade, respectively. A smaller relative loss was found in the Northern region (2.3%/decade). The negative HN trends can be mainly explained by an increase of TMEAN by 0.15°C/decade. Most of the decrease in HN occurred mainly between 1980 and 2020, as a result of a more pronounced increase in TMEAN. This is also confirmed by the change of the running correlation between HN and TMEAN, NAO, AO over time, which until 1980 were not correlated at all, while the correlation increased in later years. This suggests that in more recent years favourable combinations of temperature, precipitation, and atmospheric pattern have become more crucial for snowfall to occur. On the other hand, no correlation was found with the AMO index.

Snowfall and snow accumulation play a crucial role in shaping ecosystems and human activities in the Alpine region. This resource is under threat as a consequence of the visible effects of global warming, and, therefore, it appears urgent to understand how snowfall trends have changed in time and space. In this context, we recovered data from over a hundred snowfall (HN) time series covering the period 1980–2020 over the mountain region of Trentino‐South Tyrol in the northeastern Italian Alps and analysed them to understand snowfall climatology in the region, recent trends and their dependence on elevation and timing of the season. Negative, although not always statistically significant, trends were found in the lowest elevation range (0–1,000 m a.s.l.) over the whole winter season, while some positive and even significant trends were found from January to March above 2,000 m a.s.l. The intermediate elevation range (1,000–2,000 m a.s.l.) exhibits a strong variability with no clear trend. Negative and statistically significant trends were found in April for all elevations. An attribution analysis was performed using precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) indices. The analysis shows that, overall, P is the driver that best explains the snowfall trends, but, for low elevations, especially during mid‐winter, TMEAN is more relevant. Low elevations are facing a clear decrease in HN due to a significant increase in mean temperatures, while high elevations during mid‐winter display a slight increase in HN, associated with a general increase in precipitation. NAO and AO indices exhibit no significant correlations with HN, except at the lowest elevations and at the beginning of the season.