Effective water resource management depends on accurate projections of flows in water channels. For projected climate data, use of different General Circulation Models (GCM) simulates contrasting results. This study shows selection of GCM for the latest generation CMIP6 for hydroclimate change impact studies. Envelope based method was used for the selection, which includes components based on machine learning techniques, allowing the selection of GCMs without the need for in-situ reference data. According to our knowledge, for the first time, such a comparison was performed for the CMIP6 Shared Socioeconomic Pathway (SSP) scenarios data. In addition, the effect of climate change under SSP scenarios was studied, along with the calculation of extreme indices. Finally, GCMs were compared to quantify spatiotemporal differences between CMIP5 and CMIP6 data. Results provide NorESM2 LM, FGOALS g3 as selected models for the Jhelum and Chenab River. Highly vulnerable regions under the effect of climate change were highlighted through spatial maps, which included parts of Punjab, Jammu, and Kashmir. Upon comparison of CMIP5 and CMIP6, no discernible difference was found between the RCP and SSP scenarios precipitation projections. In the future, more detailed statistical comparisons could further reinforce the proposition.

翻译：有效的水资源管理依赖于对河道流量的准确预估。在使用预估气候数据时，不同的全球环流模型会模拟出差异显著的结果。本研究展示了为水气候影响研究遴选最新一代CMIP6中GCM的方法。遴选采用了基于包络线的方法，该方法包含基于机器学习技术的组件，使得无需现场参考数据即可完成GCM的遴选。据我们所知，这是首次针对CMIP6共享社会经济路径情景数据进行的此类比较。此外，本研究还分析了SSP情景下的气候变化效应，并计算了极端指数。最后，通过比较GCM量化了CMIP5与CMIP6数据之间的时空差异。结果为杰赫勒姆河与杰纳布河流域遴选出NorESM2 LM和FGOALS g3模型。通过空间地图突出了受气候变化影响的高度脆弱区域，包括旁遮普、查谟和克什米尔的部分地区。对比CMIP5与CMIP6发现，RCP与SSP情景的降水预估之间无明显差异。未来更详细的统计比较可进一步验证该结论。