Future changes in precipitation and temperature over the Yangtze River Basin in China based on CMIP6 GCMs

This paper explored the projected changes in precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multimodel ensemble (MME) of the bias-corrected dataset during 2025-2044 (near-term), 2045-2064 (mid-term), and 2081-2100 (long-term) periods, with reference to the baseline period 1995-2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/ decade, and 21.01 mm/decade under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. (3) Tmax (Tmin) over the YRB is projected to increase by 0.09 (0.07) degrees C/decade, 0.29 (0.27) degrees C/decade, and 0.66 (0.64) degrees C/decade under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. In the long-term, Tmax (Tmin) averaged over the YRB is projected to increase by 1.75 (1.50) degrees C, 2.72 (2.54) degrees C and 5.04 (4.85) degrees C under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections.