How Useful Are Regional Climate Models For Downscaling Seasonal Forecasts?

A longstanding yet very important question concerns the additional value derived from labor intensive regional climate models (RCMs) nested within GCM seasonal forecast models, over and above simple statistical methods of downscaling. This paper compares the two types of downscaling of precipitation "hindcasts" over the data-rich region of the Philippines, using observed data from 77 raingauges for the April-June monsoon onset season. Spatial interpolation of RCM and GCM grid box values to station locations is compared with cross-validated regression-based techniques such as canonical correlation analysis. The GCM "hindcasts" are formed from an ensemble of simulations from the ECHAM4.5 model at T42 resolution made with observed SSTs prescribed, over the 1977-2004 period. The RegCM3 with 25km resolution is nested within each of a 10-member GCM ensemble over the Philippines. To first order, we find that anomaly correlation skill at the station scale for simulations of seasonal total rainfall and monsoon onset date is quite similar using all the techniques considered, including simple spatial interpolation of the GCM values. The RCM has significantly smaller RMS error than the "raw" interpolated GCM, although statistical correction can greatly improve the latter. We examine the role of the availability of sufficiently long records of observed data as a deciding factor, which enters as a means to validate both types of the hindcasts, while being needed in addition to train the more "data hungry" statistical downscaling methods.