The Multivariate Adaptive Constructed Analogs(MACA)(Abatzoglou, Brown, 2011)
method is a statistical downscaling method which utilizes a training dataset
(i.e. a meteorological observation dataset) to remove historical biases and match spatial patterns in climate model output.
In this section, some details on the global climate models (GCMs) used by MACA from the CMIP5 project are given.
We have used MACA to downscale the model output from 20 global climate models (GCMs) of the Coupled Model Inter-Comparison Project 5 (CMIP5) for the historical GCM forcings (1950-2005) and the future Representative Concentration Pathways (RCPs) RCP 4.5 and RCP8.5 scenarios (2006-2100) from the native resolution of the GCMS to either 4-km or ~6-km.