Existing problems of downscaling atmospheric dynamics from global scale to regional scale lie in the correction of bias and the spatiotemporal heterogeneity capturing for the regional dynamics. Efforts have made to enhance the GCM model output by bias-correcting with up-to-date technologies, e.g., Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Quirin Schiermeier, 2012, R. H. Moss et al., 2010). In the other hands, techniques for highlight the variability of climate variables have been improved using statistical approach(Maurer, 2008; Abatzoglou, 2011).
In this study, four downscaling methods developed based on the Bias-corrected GCMs as well as different observational dataset, including 1)Direct Interpolation, 2)daily bias-corrected spatial interpolation (D-BCSI), monthly bias-corrected spatial disaggregation (M-BCSD) and dynamic regional earth system modeling, were performed the coarse-accurate transformation from bias-corrected HADGEM2-ES Model output (daily at .5*.5 degree) to the 3*3 minutes NE EaSM domain in daily and monthly scale.
Spatio-temporal analysis of the variability of precipitation was conducted over the study domain; Validation of the variables of different downscaling methods against observational datasets was being carried out for assessment of the downscaled climate model outputs. Hydrological impacts from the downscaled variables including air temperature and precipitation on the daily discharge and monthly stream flow were compared by water balance model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) simulation through a 100 years period of 21 century.
Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly streamflow simulation.