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Based on the 18 realizations of three state-of-the-art climate models participated in the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper examines the changes in climatology and trends of winter diurnal temperature range (DTR) over East Asia in response to natural and anthropogenic forcings. In the observation, the climatology of DTR during 1979–2014 displays a horizontal tile from southwest to northeast over East Asia. In addition, the observed DTR also presents an obviously declining trend in recent decades, with the magnitude of roughly −0.10 °C decade−1. The historical all-forcing simulations well reproduce the geographical distributions and magnitude of climatology and trends of winter DTR over East Asia in 1979–2014. Furthermore, the winter DTR exhibits a continuous declining trend from the historical scenario to the future SSP245 scenario over East Asia, with the most significant declining during the first half of twenty-first century (i.e., 2021–2060). Based on the individual forcing experiments, it suggests that the greenhouse gases forcing plays a primary role in driving the climatology and trends of DTR over East Asia, especially for the SSP245 scenario. Further analysis indicates that the increasing precipitation under greenhouse gases forcing largely explain the declining DTR trends over East Asia, especially over the northern East Asia both in historical and SSP245 scenarios. • The observed decreasing DTR trends are reasonably captured in the all-forcing historical simulations in CMIP6 models. • The greenhouse gas forcing plays a primary role in driving the declining DTR trends over East Asia • The declining of DTR is partly attributed to the increasing precipitation over East Asia in the greenhouse gas forcing. [ABSTRACT FROM AUTHOR]

This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) 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 multi-model 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) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °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. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [ABSTRACT FROM AUTHOR]