Future Precipitation Projection Based on Multiple Statistical Downscaling Methods — A Case Study of Tibetan Plateau - DONG Qianjin; YUAN Xin (Renmin Zhujiang)
Although the Coupled Model Intercomparison Project 6 (CMIP6) can well predict large-scale climatic factors,its effect on projecting watershed scales is still different from the measured data.The error of climate models is even bigger over the Tibetan Plateau,which is a high-altitude region with complicated terrain.Based on the historical scenario of the latest generation of high-resolution CMIP6 model and a variety of future climate emission scenarios such as SSP126,SSP245,SSP370,and SSP585,this paper conducts downscaling analysis and evaluates the projection performance of various statistical downscaling methods such as bias correction,KNN,and SDSM.On this basis,the best statistical downscaling method is used to project future precipitation over the Tibetan Plateau, and the spatial-temporal evolution characteristics of the projected precipitation are analyzed and compared with the historical precipitation over the Tibetan Plateau.The results reveal that the applicability amongst the three statistical downscaling methods in the Tibetan Plateau is large,with the linear regression downscaling method performing the best,followed by the bias correction method and the KNN analogy method.According to the analysis of future precipitation projections,the average precipitation and extreme precipitation over the Tibetan Plateau in the next 80 years will exhibit an overall upward trend,although the rise will be slight,and the spatial distribution will not change much.The results can provide a scientific foundation for the evaluation,planning,and management of water resources on the Tibetan Plateau.