## 1 Introduction
Methods have been proposed for estimating ET\({}_{0}\) with a reduced set of variables, such as temperature [25] or temperature and radiation [26]. However, these methods introduce empirical parameters that need to be locally calibrated, e.g., [27; 28] and then possibly regionalized [29; 30; 31]. Moreover, these empirical equations are more exposed to errors in case of non-stationarities as those induced by climate change [32]. To overcome the unavailability of ground data, radiometers mounted on satellite platforms, such as Meteosat second generation (MSG), have been also exploited for assessing downward solar radiation [33] and then combined with other sources of air temperature data for assessing ET\({}_{0}\), e.g., [34]. During the last decade, reanalysis products have been increasingly used as gridded data source for water resource management studies. Reanalysis data are produced by running updated versions of the numerical weather prediction models which sequentially assimilate observed atmosphere and surface data, to reconstruct land surface, oceans and atmosphere state variables of the past. Differently from gridded data derived by geostatistical spatial interpolation, the spatial structures of the weather variables (such a temperature, surface pressure and water vapor) are the result of the integration of the physical laws embedded into the numerical models. Two aspects have been favoring the application of reanalysis data in water resources management studies [35]: (i) their consistency in space and time, covering several decades at global or regional scales; (ii) their free public availability by means of dedicated web platforms, which make these data ready to be used in standard formats, thus avoiding all those time-consuming procedures required for collecting and homogenizing weather station data from different service providers. Examples of reanalysis are the National Center for Environmental Prediction-National Center for Atmospheric Research reanalysis (NCEP/NCAR) [36], the NASA-Modern Era Retrospective-Analysis for Research and Applications (MERRA) [37] and the ERA-Interim produced by the European Centre for Medium-range Weather Forecasts (ECMWF) [38]. A few studies have applied and compared reanalysis data for ET\({}_{0}\) estimates in different regions of the world. [PERSON] et al. [39] computed ET\({}_{0}\) by using ERA-Interim reanalysis, verifying their accuracy for water balance studies in northeastern France. [PERSON] et al. [40] verified that ET\({}_{0}\) estimated with ERA-Interim, outperformed those obtained with NCEP/NCAR in England. These results were confirmed in similar studies conducted in the Iberian Peninsula [41]. As with all numerical weather products, reanalysis data must be bias-corrected prior to applying them to a specific region [42; 43; 44]. [PERSON] et al. [45] provided a comprehensive analysis of the impact of different bias correction methods applied to ERA-Interim reanalysis data for ET\({}_{0}\) estimates across Continental Portugal. The spatial and temporal resolutions of reanalysis data, along with their reliability, have progressively increased thanks to the continuous advances in numerical weather modeling, computational and ICT tools, data assimilation techniques and increasing quality of atmosphere and ground data retrieved from satellite constellation [46]. In 2018 the UERRA-HARMONIE system was released, producing reanalysis data with a spatial resolution of 11 km over the entire European area [47]. The boundary conditions of UERRA-HARMONIE system are defined by the ERA-Interim reanalysis data which has a spatial resolution of 79 km. UERRA-HARMONIE covers the period from January 1961 until July 2019, when ERA-interim production ceased. UERRA-HARMONIE is integrated by a complementary surface analysis system, named MESCAN-SURFEX, which combines downscaled UERRA-HARMONIE reanalysis fields and additional surface observations, to produce a coherent surface analysis over Europe with a spatial resolution of 5.5 km [48].