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Background: 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. Results: 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. Conclusions: 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.

This study provides an assessment of changes in mean and extreme climate in northeast Thailand, focusing on the near‐future period (2021–2050). Spatiotemporal changes in climate extremes and return values are investigated compared to 1981–2010. Climate model‐related uncertainties are quantified using 14 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 8 models from phase 6 (CMIP6). CMIP6 models have a higher sensitivity to external forcings as the CMIP6 ensemble suggests an increase in maximum and minimum temperatures by 1.45°C (0.8–1.9°C) and 1.54°C (1.1–1.9°C) under the high emission scenario, which is greater than by CMIP5 ensemble: 1.10°C (0.5–1.7°C) and 1.13°C (0.7–1.6°C), respectively. No significant changes in annual rainfall are projected, although it will be temporally more uneven with decreases (6–11%) during the pre‐rainy season (March–May) and increases (2–8%) during the rainy season (June–October). The bootstrap analysis technique shows the inter‐model uncertainties for rainfall projections in CMIP6 have reduced by 40% compared to CMIP5. The annual number of hot days will increase more than twofold and warm nights, more than threefold. Near‐future will experience an increase in the rainfall intensity, a decrease in the number of rainy days, and an increase in the 20‐year return values of annual maximum 1‐day rainfall and consecutive 5‐days rainfall (>30%). In addition, the rainy season will be shortened in the future as onset and retreat are delayed, which may have implications in agricultural activities in the basin since cultivation is primarily rainfed. These findings suggest that anthropogenic activities will significantly amplify the climate extremes. The study results will be useful for managing climate‐related risks and developing adaptation measures to improve resilience towards potential climate hazards.

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.

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