In this study, the performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) global climate models (GCMs) in simulating precipitation over the Tibetan Plateau (TP) was assessed using data from 1961 to 2005 by an improved score-based method, which adopts multiple criteria to achieve a comprehensive evaluation. The future precipitation change was also estimated based on the Delta method by selecting the submultiple model ensemble (SMME) in the near-term and far future (2051-2095) periods under Representative Concentration Pathways (RCP) scenarios RCP4.5 and RCP8.5. The results showed that most GCMs can reasonably simulate the precipitation pattern of an annual cycle; however, all GCMs overestimated the precipitation over TP, especially in spring and summer. The GCMs generally provide good simulations of the temporal characteristics of precipitation, while they did not perform as well in reproducing its spatial distributions. Different assessment criteria lead to inconsistent results; however, the improved rank score method, which adopts multiple criteria, provided a robust assessment of GCMs performance. The future annual precipitation was projected to increase by~6% in the near-term with respect to the period 1961-2005, whereas increases of 12.3% and 16.7% are expected in the far future under RCP4.5 and RCP8.5 scenarios, respectively. Similar spatial distributions of future precipitation changes can be seen in the near-term and far future periods under the two scenarios, and indicate that the most predominant increases occurred in the north of TP. The results of this study are expected to provide valuable information on climate change, and for water resources and agricultural management in TP.

The performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs) in temperature simulations in the Tibetan Plateau (TP) was comprehensively assessed by an improved score-based and multiple-criteria method using data collected from 1961 to 2005. Future temperatures were also simulated based on a multimodel ensemble coupled with the Delta downscaling method for near-term (2006-2050) and long-term (2051-2095) projections under Representative Concentration Pathways (RCP, scenarios RCP4.5 and RCP8.5). Our results demonstrated that all the GCMs evaluated in our study could capture the seasonal temperature patterns. However, most GCMs tended to underestimate temperatures by an average of −2.0°C. All the GCMs could effectively simulate temporal distribution, with a mean correlation coefficient of 0.997. However, they did not perform well in reproducing spatial distribution. Different assessment criteria lead to inconsistent results; however, the improved rank score method with multiple criteria provided a robust assessment of GCMs performance. MPI-ESM-LR, CMCC-CMS, and GFDL-ESM 2M showed a better temperature simulation performance compared to the other GCMs that we have assessed. Topographic correction could effectively enhance spatial distribution simulation; however, this increased temperature underestimation. Future temperatures were projected to increase by 1.4°C and 1.6°C in near-term, and by 2.4°C and 4.0°C in long-term under RCP4.5 and RCP8.5 scenarios, respectively. High temperatures mainly occurring in the southeastern end of the Himalayas, as well as the northern and southeastern margins of the TP. The results are expected to provide valuable information on climate change and its impact on hydrology, ecology, and socioeconomics of the TP.