## 4 Discussion and Conclusions  
Regional maps of daily reference evapotranspiration are fundamental for addressing many water resources management issues, especially in those areas, such in Mediterranean regions, where significant freshwater abstractions are practiced for crop irrigation in the dry seasons [4]. The computation of FAO-56 daily reference evapotranspiration (ET\({}_{0}\)) requires dataset of multiple weather variables, recorded by networks of automatic weather stations (AWSs). The spatial distribution of these AWSs is generally too coarse and irregular for assessing the spatial variability of weather variables, such as solar radiation and wind [76]. The availability of observed weather data is also hindered by the policy data access adopted by the hydrometeorological service providers. This study examined the case of the Campania Region (Southern Italy), where the Regional Hydrometeorological Service is operating a network of 18 automatic weather stations, recording a complete set of weather data since year 2008, across an area of 14,000 km\({}^{2}\). The spatial density of this network is relatively high if compared with those examined by previous studies on this topic: [PERSON] et al. [45] analyzed data of 24 stations in Portugal (about 92,000 km\({}^{2}\)) while [PERSON] et al. [41] collected data observed at 130 station across the entire Iberian Peninsula (597,000 km\({}^{2}\)). Indeed, the access to observed weather data can be even more difficult for areas extending across regional or national boundaries. In Italy, for instance, weather monitoring networks are managed by regional and national services, without a common data sharing policy and data distribution platform [77]. Pan European gridded agrometeorological maps released to support the EU Common Agricultural Policy, are derived by interpolating observed weather data collected in the different Member States, with uneven distributions, not consistent with what would be required based on the topographic complexity (e.g., Agri4 Cast Resources Portal, Gridded Agro-Meteorological Data in Europe [78]). These constrains in the availability of observational weather data are the main motivations of the growing interest toward the application of reanalysis data in hydrological studies. The quality of the reanalysis data is rapidly growing, thanks to the significant research efforts in providing temporally consistent combination of the most advanced numerical weather models and the available climate observations. This study examined the last two reanalysis products distributed by the Copernicus Climate Change Service: the pan-European regional reanalysis UERRA MESCAN-SURFEX (UMS) with a spatial resolution of 5.5 km; and the global land surface reanalysis ERA5-Land (E5L) with a spatial resolution of nine kilometers. We found that ERA5-Land systematically outperformed UERRA MESCAN-SURFEX in reconstructing the historical weather variables required for computing ET\({}_{0}\). This result testifies that the choice of the gridded product should be not merely driven by its nominal spatial resolution. ERA5-Land benefits of the latest ERA5 global reanalysis, downscaled from 31 km to 9 m by applying the lapse rate correction to account for the influence of the altitude on the spatial structure of the weather variables. UMS is instead a downscaled product of UERRA-HARMONIE regional reanalysis at spatial resolution of 11 km, which boundary conditions were defined by the ERA-Interim global reanalysis, which production ceased in 2019, being substituted by the more advanced ERA5. Reanalysis products are generally evaluated by point-to-point comparisons, consisting in assessing the difference between the observed data and the corresponding reanalysis value, obtained by linear interpolation of the reanalysis data at the numerical grid points surrounding the observation site, e.g., [41; 45].