In this study, 28 climate models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and 32 models from the sixth phase (CMIP6) have been evaluated for their ability to simulate large‐scale atmospheric circulations (using rainfall, wind fields, geopotential height, temperature, and moisture flux convergence) for the summer monsoon in Southeast Asia. Using a multi‐criteria decision making technique, models have been ranked based on 25 metrics which compare their performances with observation data. Results indicate a better representation of annual rainfall cycles as well as spatial pattern by CMIP6 models compared to CMIP5. Though majority of the models from both CMIPs show late onset and early retreat of the rainy season, CMIP6 GCMs simulate the onset, retreat, and the length of the rainy season closer to the observation. Large‐scale circulation patterns evaluated using spatial correlation and root mean square error (RMSE) show improvements in CMIP6 across all metrics, especially for the moisture flux convergence. Performances for large‐scale circulation are generally reflected in rainfall simulation; however, few models showed that better simulations of rainfall do not exclusively depend on their performance for large‐scale variables. Overall, CMIP6 models are found to be superior to CMIP5 models in simulating rainfall and large‐scale circulation, which is likely attributable to CMIP6 model's higher spatial resolutions, increased number of vertical levels, improved atmospheric and land surface parameterization, etc. Finally, subsets of optimal models from CMIP5 and CMIP6 that proved to be better at representing the summer monsoons in the study area are identified. These models are recommended to develop robust future projections that can be used for climate change impact and adaptation studies.

There is a very high uncertainty in the future climate change in the Himalayas and few studies has been carried out towards predicting future climate scenario in the Nepal Himalayas. In this study, climate change projection has been carried out for the Marsyangdi River Basin in the Nepal Himalaya which is focused on quantifying impacts of climate change with meteorological parameters (temperature and precipitation) for the future period, based on the outputs from fifth assessment report of Intergovernmental Panel on Climate Change. The study makes use of CanESM2 dataset which are statistically downscaled using statistical downscaling model (SDSM). Climate projections are available for three representative concentration pathways (RCPs) namely RCP 2.6, RCP 4.5 and RCP 8.5 for up to 2100. The study revealed that both the temperature and precipitation will increase for three RCPs in future. Compared to the baseline period, the annual average of maximum temperature has been projected to increase by 0.82 °C, 1.35 °C and 2.29 °C by 2090s, while, annual average of minimum temperature has been projected to increase by 0.87 °C, 1.44 °C and 2.43 °C by 2090s for RCP 2.6, RCP 4.5 and RCP 8.5 respectively. Similarly, annual average precipitation has been projected to increase by 4, 14 and 21 % by 2090s for RCP 2.6, RCP 4.5 and RCP 8.5 respectively. The projected percentage increase in annual precipitation has been found to have inverse relationship with the elevation. The study suggests that climate change is evident in the study area and these findings will be useful in climate change impact assessment in different sectors such as geodisasters and future management strategies in the Marsyangdi River Basin.

Climate change (CC) and land-use change (LUC) will alter a basin’s hydrological processes and water balance. Quantifying their significance is imperative in formulating appropriate countermeasures and management plans. This study assesses projected changes in hydrological variables under CC and LUC scenarios to provide multi-dimensional insight into water balance relevant to an agricultural watershed in Northeast Thailand. The soil and water assessment tool (SWAT) is utilized to simulate hydrological variables (evapotranspiration, soil moisture, surface runoff, and water yield) for the baseline (1981–2010) and the near-future (2021–2050) after calibrating the model. CC projections considering the CMIP6 model ensemble for the high-emission scenario (SSP5-8.5) show the annual rainfall may not change significantly (0.5% increase compared to baseline). However, the surface runoff will likely increase by 33% due to the projected increase in rainfall intensity and extremes. The increase in urban areas due to LUC for the business as usual (BAU) scenario is also expected to boost surface runoff by 38–87%. Similarly, the LUC scenario with forest expansion will increase evapotranspiration by up to 1.3%. While CC is anticipated to raise water yield by 11%, LUC may reduce it by 6%. Under the combined scenario, the yield is expected to increase by 8%, indicating CC as the dominating driver. The results show that although CC governs the runoff from the basin, land-use change will also impact flows at a monthly scale. Importantly, soil moisture in the future will decrease (8–9%) under both stressors, which will reverberate in the basin’s agricultural livelihood and socioeconomic settings unless the appropriate adaptation measures are implemented.