In recent years, stable isotopes of the water molecule (oxygen-18 and deuterium) have become a useful tool for tracking the water cycle. The concentration of these tracers changes with variations of water molecules within the water cycle. Due to this feature of isotopes, global large-scale isotope models have been developed. On the other hand, numerous local and global networks have been created in order to monitor the concentration of precipitation isotopes. The main problem with the simultaneous use of these local stations and the large-scale isotope datasets is their temporal and spatial mismatch. To use both isotope databases for monitoring the hydrological cycle in local scale, it is necessary to downscale the large-scale models' outputs. In this research, a downscaling approach is proposed for isotopes' concentrations using three statistical models, including multiple linear regression, generalized linear and weighting least square regression models. The results indicate that the implementation of the statistical downscaling method in the case of information preprocessing based on the seasonal changes, their spatial variations and a suitable method selection is a useful tool for monitoring the climate changes of a region according to the information on the stable oxygen-18 isotope.