## 4 Discussion and Conclusions
In this study, we aimed at assessing the reanalysis performances with what could be achieved by spatially interpolating AWSs observations, following established methods in regional scale analyses [75; 79]. AWSs data were interpolated by universal kriging and ordinary kriging, combined with a cross validation procedure for retrieving the corresponding indicators of performances, which were then compared with the reanalysis products. Reanalysis temperature and wind speed exhibited better performances than other variables, and comparable to those achieved by kriging interpolations (Figure 4). Indeed, as a general aspect of numerical weather predictions, prognostic variables such as temperature and wind speed, since they are directly derived by the numerical integration of dynamic laws of the atmosphere, are better predicted than diagnostic variables. Our findings confirm that the numerical solar radiation reconstructions are most prone to error. UMS estimate errors of solar radiation were almost 2-3 times larger than E5L. UMS results were coherent with other studies that evidenced ERA-Interim tendency to overestimate solar radiation [45; 80]. Improvement in solar radiation estimates is certainly one of the most desirable aspect from future reanalysis products, such as the upcoming Copernicus regional reanalysis for Europe (CERRA) [57], especially because solar radiation errors have the highest impact on the predicted evapotranspiration [52; 81]. The availability of weather data on grid points located over the sea close to the coastline would be also desirable, to avoid the issue experienced when assessing weather conditions close to the coast with E5L (Figure 2b). Still, ET\({}_{0}\) estimate performances based on the examined reanalysis data were satisfactory if compared with those obtained by spatially interpolating the observed data. Reanalysis-based ET\({}_{0}\) presented average nBIAS lower than 5%. Average nRMSE ranged from 17% with E5L to 22% with UMS. The values of these performance indicators were not far from those obtained by kriging interpolation: average nBIAS of 1% and average nRMSE of 14%. ET\({}_{0}\) estimate performances based on E5L and UMS were better than those presented by [PERSON] et al. [41] for the Iberian Peninsula based on NCEP/NCAR blended reanalysis data, and those presented by [PERSON] et al. [45] for Continental Portugal based on the ERA-Interim. Another interesting result is that differences in statistical performances between reanalysis- and observation-based ET\({}_{0}\) estimates were small: ET\({}_{0}\) predicted with E5L and the kriging estimator presented average differences in nRMSE smaller than 3%; while in the comparison between UMS and kriging estimator, the differences in nRMSE were smaller than 8%. The corresponding differences were even smaller and negligible in terms of nBIAS. These outcomes confirm that for regions with limited past weather data archives or served by sparse and irregular monitoring networks, reanalysis data can be successfully exploited as a source of gridded weather data. Nevertheless, the availability of ground-based weather observations always represents an added value that must be used in combination (and not in substitution) with reanalysis data, to improve our knowledge of the past weather in a region. A preliminary bias correction of the reanalysis products is always advisable if observational data are available in the region. In this study, we used a simple regionally uniform bias correction, applied to all variables except for wind speed, for which we did not find any systematic bias pattern, consistently with previous reports [82]. Indeed, the development of different procedures for improving the bias-correction has been the topic of several research studies [43; 44; 45; 76]. However, we believe that this topic must be addressed in a more general research framework, aimed at evaluating the optimal integration of observational and reanalysis data for regional hydrological studies [76]. **Author Contributions:** Conceptualization, A.P. and G.B.C.