Many projects are underway around the world to downscale the data from Global Climate Models (GCM). Most of the statistical tools have a lengthy procedure to follow for the downscaling activity. To improve its accuracy, the GCM data is re-gridded according to the grid points of the observed data, standardized, and, in some cases, bias-removal is required. Soft tools can extensively reduce the time and effort required for these steps. The present research will provide the audience with an understanding of the capabilities of a soft tool, Model Trees (MT), as a statistical downscaling tool. The current work suggests that future precipitation can be predicted by using precipitation data from the nearest four grid points as input to soft tools and observed precipitation as output. This research aims to estimate precipitation trends in the near future (2021–2050) for the city of Pune, in the state of Maharashtra, India. The findings indicate that MT can model the precipitation with excellent accuracy compared to the traditional method of Distribution Based Scaling (DBS). For this purpose, 5 GCMs were used, and the results show that MT overpowers DBS in downscaling the climate data. The detailed methodology and results are discussed in the larger paper.