Statistical downscaling is a method used to derive local or regional climate information from larger-scale climate models, such as those produced by General Circulation Models (GCMs). It involves using statistical techniques to relate large-scale atmospheric variables to local climate variables, allowing for more precise predictions of climate impacts at smaller scales. This approach is particularly useful in climate change studies, where understanding local effects is crucial for planning and adaptation. By analyzing historical climate data, researchers can create models that project future conditions, helping communities prepare for changes in temperature, precipitation, and other climate-related factors.