Despite of their increasing importance as inputs to models for a wide range of scientific fields, high-resolution meteorological variables are not recorded very often on spatially regular grids. This problem is usually overcome by using data from reanalysis models, although they are less accurate. This paper discusses the development of a new spatial downscaling methodology to provide high-resolution maps of daily maximum and minimum air temperature. The application of this approach provides thorough observations in sparsely sampled areas by combining the accuracy of measurements from ground-based stations with the high availability and uniformity of model-based data. The dataset includes more than a decade (2003–2013) of data collected at 113 stations, about 30% of which constituted an independent set for the validation procedure. The efficacy of this approach is evaluated using statistical scores that are regularly employed in model evaluation studies and the improvements over the classical approach are remarkable. The results show that overall the our #hybrid# method provides fair estimates of temperature values. Particularly, MBE is less than 0.29 °C and 0.60 °C for the daily maximum and minimum air temperature respectively; RMSE is less than 1.24 °C for the maximum temperature and 1.86 °C for the minimum temperature, the analysis on MAE assures that there is not contribution of the errors in the spatial variability (MAE ≈ RMSE). The correlation coefficient, close to 1 (ρ ≈ 0.97), indicates a strong positive linear relationship.