![[2602.13181] Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13181] Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins
Summary
This study evaluates the selection of CMIP6 models for projecting regional precipitation and assessing climate change impacts in the Jhelum and Chenab River Basins, utilizing machine learning techniques for model selection.
Why It Matters
Accurate climate projections are crucial for water resource management, especially in vulnerable regions. This research provides insights into model selection for climate assessments, which can influence policy and adaptation strategies in response to climate change effects.
Key Takeaways
- The study employs machine learning methods to select CMIP6 models for climate projections.
- It highlights the vulnerability of regions in Punjab, Jammu, and Kashmir to climate change impacts.
- No significant differences were found between CMIP5 and CMIP6 precipitation projections under different scenarios.
Physics > Atmospheric and Oceanic Physics arXiv:2602.13181 (physics) [Submitted on 13 Feb 2026] Title:Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins Authors:Saad Ahmed Jamal, Ammara Nusrat, Muhammad Azmat, Muhammad Osama Nusrat View a PDF of the paper titled Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins, by Saad Ahmed Jamal and 3 other authors View PDF HTML (experimental) Abstract: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 C...
