Complexity-reduction modelling can be useful for increasing the understanding of how the climate affects basin soil moisture response upon historical times not covered by detailed hydrological data. For this purpose, here is presented and assessed an empirical regression-based model, the European Soil Moisture Empirical Downscaling (ESMED), in which different climatic variables, easily available on the web, are addressed for simplifying the inherent complexity in the long-time studies. To accommodate this simplification, the Palmer Drought Severity Index, the precipitation, the elevation and the geographical location were used as input data in the ESMED model for predicting annual soil moisture budget. The test area was a large region including central Europe and Mediterranean countries and the spatial resolution was initially set at 50 km. ESMED model calibration was made according with the soil moisture values retrieved from the Terrestrial Water Budget Data archive by selecting randomly 285 grid points (out of 2606). Once parameterized, ESMED model was performed at validation stage both spatially and temporally. The spatial validation was made for the grid points not selected in the calibration stage while the comparison with the soil moisture outputs of the GLDAS-NOAH10 simulations upon the period 1950-2010 was carried out for the temporal validation. Moreover, ESMED results were found to be in good agreement with a root-zone soil moisture product obtained from active and passive microwave sensors from various satellite missions. ESMED model was thus found to be reliable for both the temporal and spatial validation and, hence, it might represent a useful tool to characterize the long-term dynamics of soil moisture-weather interaction.