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Global warming has caused severe regional water security risks, and from the theory of the hydrological cycle, the daily extreme temperature could also bring an impact on the streamflow volume, which could be even more important than the average temperature. Therefore, based on the level of the maximum or minimum temperature warming scenarios, a variety of meteorological datasets were selected to assess the asymmetric effect of increased daily extreme temperature on the streamflow from a multi-scale perspective by using the Variable Infiltration Capacity (VIC) model. Model simulations indicate that the streamflow experiences more significant changes in response to the maximum temperature than in response to the minimum temperature, and the relationships of streamflow with both the maximum and minimum temperatures show an upwards parabolic response function, but the response function varies with the type of warming. Additionally, the seasonal and monthly duration curves results show that the increases in both the maximum and minimum temperatures demonstrate a similar response that leads the proportion of the flood period streamflow to be increased (Tmin : 0.16–0.53%/°C; Tmax : 0.11–0.51%/°C). When the minimum temperature increases, the higher the original temperature in the region, the greater the proportion of the flood period streamflow increases. However, when the maximum temperature increases, the opposite effect occurs. • This paper included a numerical analysis of the sensitivity of streamflow to daily extreme temperature increases. • This paper discusses in detail the regional asymmetric effect of daily extreme temperature increase to streamflow. • A relatively complete picture of the hydrological processes caused by daily extreme temperature rise is presented. [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]