A general method for validating statistical downscaling methods under future climate change

Statistical downscaling methods (SDMs) are often used to increase the resolution of future climate projections from coupled atmosphere-ocean general circulation models (GCMs). However, SDMs are not able to capture small-scale dynamical changes unresolved by GCMs. For this reason, we propose a two-step generalized validation process to evaluate the performance of any statistical downscaling method relative to regional climate model (RCM) simulations driven by the same GCM fields. First, we compare historical station-based observations with simulations obtained from a statistical model fitted to and driven by reanalysis fields, and then driven by historical GCM fields. Then, the SDM is required to produce future projections consistent with RCM simulations used as pseudo-observations under future emissions scenarios. Using the climate extension of the fifth generation PennState/ NCAR Mesoscale Model (CMM5) driven by NCAR/ DOE Parallel Climate Model (PCM) simulations, we apply this method to identify the strengths/ weaknesses of a nonhomogeneous stochastic weather typing method.