Dynamical downscaling projections of extreme temperature for the major river basins in China under shared socioeconomic pathway scenarios

Abstract

AbstractThis study presents an analysis of the changes in extreme temperatures over major river basins in China under different shared socioeconomic pathway (SSP) scenarios based on a set of dynamical downscaling over CORDEX East Asia, performed with the regional climate model RegCM4 at a resolution of 25 km, driven by the global climate model FGOALS‐g3. The results indicated that the dynamical downscaling tended to reduce the warm (cold) biases of the present‐day daily maximum (TXx) and minimum temperatures (TNn) simulated by FGOALS‐g3. Under the SSP scenarios, substantial increases in TXx and TNn and the number of warm days and warm nights throughout China were projected by both models. The average increase in TXx in most of the river basins ranged from 1 to 2°C under the SSP1‐2.6 scenario, from 2 to 3°C under the SSP2‐4.5 scenario, and from 3 to 5°C under the SSP5‐8.5 scenario. Reductions in the number of cold days and cold nights were projected across China, but these reductions were smaller than the increases in the number of warm days and warm nights. The interregional variability in the increases in TXx and TNn was more pronounced in the SSP5‐8.5 scenario and the RegCM4 model than in the other SSP scenarios and the FGOALS‐g3 model, respectively. Larger percentages of the land area and population of China would be affected by greater increases in extreme high temperatures and TNn. Under the SSP2‐4.5 (SSP1‐2.6) scenario, 20–65% (50–95%) of the impacts of extreme temperatures under the SSP5‐8.5 scenario could be avoided across most regions in China. The avoided impacts of warm days and warm nights were greater than those of the other extreme temperature indices. More of the impacts of extreme temperatures could be avoided in the Huai River basin, Yellow River basin, Hai River basin, and Northwest Interior River basin than in the other river basins in China.