When running SDSM what is the best way to choose the predictors?
As suggested in various studies, the choice of predictor variables is one of the most influential steps in the development of statistical downscaling procedure. The ideal predictor must be strongly correlated with the target variable (i.e. predictand), physically sensible and plausible, well represented in the GCM control run, and capture multi-year variability. In other words, predictors relevant to the local predictand should be adequately reproduced by the host climate model at the temporal and spatial scales used to condition the downscaled response. Prior knowledge of climate model limitations is necessary when screening potential predictors to prevent the introduction of biases in the downscaling procedure. Other complementary work must be done to systematically evaluate the accuracy of other GCM predictors, but this work is time-consuming as the size and positioning of the predictor field vary seasonally and spatially. There is a lot of guidance on choosing predictors in the SDS
