CR 72:141-151 (2017)  -  DOI: https://doi.org/10.3354/cr01460

ABSTRACT: Reconstructing the multisite and multivariate correlations of meteorological variables during downscaling of general circulation model (GCM) outputs is extremely important for hydrological variability simulation. To this end, this study proposes a 2-step approach to downscale GCMs considering the correlations between sites and variables. The first step generates single-site climate change scenarios for daily precipitation and maximum and minimum temperature. This step is open to any downscaling technique, since the subsequent correlation reconstruction does not change the statistical parameters. This study employed a quantile mapping method for spatial downscaling and the Richardson-type weather generator for temporal downscaling. The second step re-correlates the generated daily and site-based climate change scenarios by a distribution-free shuffling procedure. We used precipitation and maximum and minimum temperature data from a network of 15 weather stations and 1 GCM to develop and evaluate the method. Results showed that the proposed method satisfactorily preserved the downscaled statistical parameters and intersite and intervariable correlations. By simultaneously shuffling the data for all stations and all 3 variables for each month, the proposed approach is easy to implement, as well as being computation-saving, and provides a rational option for multisite and multivariate GCM downscaling.

KEY WORDS: GCM downscaling · Spatial structure · Temporal pattern · Weather generator