资源环境科技发展态势分析平台

Four statistical downscaling methods, that is, three quantile mapping based techniques including bias-correction and spatial downscaling (BCSD), bias-correction and climate imprint (BCCI), and bias correction constructed analogues with quantile mapping reordering (BCCAQ), and the cumulative distribution function transform (CDF-t) method, are evaluated with daily observed precipitation and surface temperature for 1961-2005 over China. The four downscaling methods improve the accuracy over the driving general climate models (GCMs) significantly in terms of spatial variability, bias, seasonal cycle, and probability density functions of daily series and extreme events. Overall, BCSD outperforms other methods in frequency distributions of daily temperature, precipitation, and extreme precipitation indices such as wet and dry spell lengths. But it comparably has larger biases in temperature-related extremes. When downscaling the seasonal and extreme precipitation, the three quantile mapping based techniques exhibit better capacity than CDF-t in terms of the spatial correlation and bias over all subregions. Whereas CDF-t methods overestimate consecutive wet days and extreme wet indices significantly, as it displays limited improvement over the driving GCMs by producing too many drizzle days using either absolute or relative threshold. All methods are equally skillful in downscaling monthly and seasonal temperature, and the temperature extremes are better reproduced by BCCI, BCCAQ and CDF-t. However, the statistical downscaling methods show limited capacity in improving the interannual variability of temperature and precipitation extremes.