资源环境科技发展态势分析平台

In the present study, four different bias correction methods are assessed in improving the precipitation output from a set of two regional climate simulations for 1971-2005 period. The simulations come from Coordinated Regional Climate Downscaling Experiment over South Asia (CORDEX-SA). The bias correction methods applied here are linear scaling (Scaling), empirical quantile mapping (EQM), power transformation (PTR) and local intensity scaling (LOCI). All the bias correction techniques show skill in improving the model results, but there are clear differences in the degrees of success depending upon the characteristic of precipitation studieslike seasonal mean, seasonal extremes or inter-annual variability. Most of the methods show equivalently high ability in correcting the seasonal mean precipitation values; however, there also exist residual biases which vary spatially over India. The largest reduction in bias was observed for the frequency of dry days and precipitation on very wet days, which are significantly altered after adjustment by EQM and PTR. It is found that distribution-based EQM method is more skilful followed by PTR and LOCI in most of the cases including correction of frequency-based indices (e.g., dry days number and inter-annual variability).