In the region of the European Alps, national and regional meteorological services operate rain‐gauge networks, which together, constitute one of the densest in situ observation systems in a large‐scale high‐mountain region. Data from these networks are consistently analyzed, in this study, to develop a pan‐Alpine grid dataset and to describe the region's mesoscale precipitation climate, including the occurrence of heavy precipitation and long dry periods. The analyses are based on a collation of high‐resolution rain‐gauge data from seven Alpine countries, with 5500 measurements per day on average, spanning the period 1971–2008. The dataset is an update of an earlier version with improved data density and more thorough quality control. The grid dataset has a grid spacing of 5 km, daily time resolution, and was constructed with a distance‐angular weighting scheme that integrates climatological precipitation–topography relationships. Scales effectively resolved in the dataset are coarser than the grid spacing and vary in time and space, depending on station density. We quantify the uncertainty of the dataset by cross‐validation and in relation to topographic complexity, data density and season. Results indicate that grid point estimates are systematically underestimated (overestimated) at large (small) precipitation intensities, when they are interpreted as point estimates. Our climatological analyses highlight interesting variations in indicators of daily precipitation that deviate from the pattern and course of mean precipitation and illustrate the complex role of topography. The daily Alpine precipitation grid dataset was developed as part of the EU funded EURO4M project and is freely available for scientific use.

A daily high-resolution gridded climatic data set is presented for Emilia-Romagna, Italy, covering the period 1961–2010. Time series of precipitation and temperatures, from 254 and 60 locations, respectively, were first checked for quality, temporal homogeneity and synchronicity, then interpolated on a grid. For temperature, a daily best-performing detrending procedure was used, followed by the interpolation of regression residuals by means of a modified inverse distance scheme, accounting for orographic barriers. Elevation, urban fraction and topographic position are the geographical proxy parameters used for detrending. The same scheme, without detrending, was used for daily precipitation. All data were spatially interpolated on a high-resolution digital elevation model, and then averaged on a triangulated irregular grid with variable resolution depending on topography. Interpolation determined average errors between 1.0 and 1.5 °C, with higher values for minimum temperatures, in winter and for years prior to 2000. Precipitation is on average underestimated, up to 25% for intense and heavy precipitation in the summer semester. Multiple detrending improves minimum temperature estimation, while the modified distance scheme reduces interpolation errors for temperature and precipitation. The data set is mainly addressed to users and applications requiring time-averaged temperature and precipitation fields. Its main limitations concern precipitation underestimation, winter minimum temperature unexplained variance, unresolved pattern scales, station density and undetected asynchronicities. The data set shows a significant increase in mean annual temperatures all over the region, with trend values up to 0.5 °C decade⁻¹. An average, locally significant, decrease in annual precipitation is also detectable, mostly over the western mountains (−100 mm decade⁻¹), while significant increases are identified in some areas close to the Po River Delta. Local spatial patterns may, however, be susceptible to large errors, especially in low trend areas.