Modeling of historical and future changes in temperature and precipitation in the Panj River Basin in Central Asia under the CMIP5 RCP and CMIP6 SSP scenarios
Gulakhmadov A. Chen X. Gulahmadov N. Rizwan M. Gulakhmadov M. Nadeem M.U. Rakhimova M. Liu T.
December 2025Nature Research
Scientific Reports
2025#15Issue 1
This study examines the complexities of climate modeling, specifically in the Panj River Basin (PRB) in Central Asia, to evaluate the transition from CMIP5 to CMIP6 models. The research aimed to identify differences in historical simulations and future predictions of rainfall and temperature, examining the accuracy of eight General Circulation Models (GCMs) used in both CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5). The evaluation metrics demonstrated that the GCMs have a high level of accuracy in reproducing maximum temperature (Tmax) with a correlation coefficient of 0.96. The models also performed well in replicating minimum temperature (Tmin) with a correlation coefficient of 0.94. This suggests that the models have improved modeling capabilities in both CMIPs. The performance of Max Plank Institute (MPI) across all variables in CMIP6 models was exceptional. Within the CMIP5 domain, Geophysical Fluid Dynamics (GFDL) demonstrated outstanding skill in reproducing maximum temperature (Tmax) and precipitation (KGE 0.58 and 0.34, respectively), while (Institute for Numerical Mathematics) INMCM excelled in replicating minimum temperature (Tmin) (KGE 0.28). The uncertainty analysis revealed a significant improvement in the CMIP6 precipitation bias bands, resulting in a more precise depiction of diverse climate zones compared to CMIP5. Both CMIPs consistently tended to underestimate Tmax in the Csa zone and overestimate it in the Bwk zone throughout all months. Nevertheless, the CMIP6 models demonstrated a significant decrease in uncertainty, especially in ensemble simulations, suggesting improvements in forecasting PRB climate dynamics. The projections revealed a complex story, as the CMIP6 models predict a relatively small increase in temperature and a simultaneous drop in precipitation. This indicates a trend towards more uniform temperature patterns across different areas. Nevertheless, the precipitation forecasts exhibited increased variability, highlighting the intricate interaction of climate dynamics in the PRB area under the impact of global warming scenarios. Hydrological components in global climate models can be further improved and developed with the theoretical reference provided by this study.
CMIPs , Maximum Temperature , Minimum Temperature , Panj River Basin , Precipitation
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State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Institute of Water Problems, Hydropower, and Ecology of the National Academy of Sciences of Tajikistan, Dushanbe, 734042, Tajikistan
Department of Hydraulics and Hydro Informatics “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers”, National Research University, Tashkent, 60111496, Uzbekistan
Department of Civil Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan, Pakistan
Climate, Energy and Water Research Institute, National Agriculture Research Center, Islamabad, 44000, Pakistan
Department of Engineering Mechanics and Energy, System and Information Engineering, University of Tsukuba, Ibaraki, 305-8577, Japan
Space Technologies and Remote Sensing Center, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
College of Geoinformatics, Zhejiang University of Technology, Hangzhou, 310014, China
State Key Laboratory of Desert and Oasis Ecology
University of Chinese Academy of Sciences
Institute of Water Problems
Department of Hydraulics and Hydro Informatics “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers”
Department of Civil Engineering
Climate
Department of Engineering Mechanics and Energy
Space Technologies and Remote Sensing Center
College of Geoinformatics
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