ABSTRACT
Spatial downscaling is performed to translate the bias-corrected global climate model (GCM) coarse output of the observation period to the GCM output of the prediction period via interpolation. This chapter describes three spatial downscaling models with the example for the Florida monthly precipitation with mean daily rate as bias correction and spatial downscaling (BCSD), bias correction and constructed analogue (BCCA), and bias correction and stochastic analogue (BCSA). Since each model employs a different statistical technique, its output also has unique statistical characteristics and a caution must be exercised to employ in application. It has been reported that the BCSD model has been successfully used to downscale GCM results and to assess hydrological impacts of climate change. The BCCA method obtains the spatial information from a linear combination of fine-scale historical analogues for downscaling the coarse-scale GCM outputs.
