Statistical downscaling is a method used to refine large-scale climate data into more localized information. It involves using statistical techniques to relate broad climate models, such as those from General Circulation Models (GCMs), to specific regional or local climate variables. This helps in understanding how global climate changes may impact local weather patterns. The process typically requires historical climate data from a specific area, which is then correlated with the larger-scale climate projections. By applying these statistical relationships, researchers can generate more precise forecasts for variables like temperature and precipitation, aiding in climate adaptation and planning efforts at the local level.