What is downscaling?

Downscaling is the general name for a procedure to take information known at large scales to make predictions at local scales. The two main approaches to downscaling climate information are dynamical and statistical. Dynamical downscaling requires running high-resolution climate models on a regional sub-domain, using observational data or lower-resolution climate model output as a boundary condition.  These models use physical principles to reproduce local climates, but are computationally intensive.  Statistical downscaling is a two-step process consisting of i) the development of statistical relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors (e.g., pressure fields), and ii) the application of such relationships to the output of global climate model experiments to simulate local climate characteristics in the future. More information about the method used to downscale CCSM-3 projections is available in this white paper.