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CMIP6 Climate Model Ensemble-Based Assessment of Future Changes in Air Temperature and Atmospheric Precipitation over Uzbekistan under SSP Scenarios

Authors

  • Bakhtiyar M. Kholmatjanov ORCID Hydrometeorological Research Institute, Tashkent 100052, Uzbekistan; Central Asian University of Environmental and Climate Change Studies (Green University), Tashkent 111104, Uzbekistan
  • Ergashali Y. Rakhimov Email ORCID Hydrometeorological Research Institute, Tashkent 100052, Uzbekistan; Central Asian University of Environmental and Climate Change Studies (Green University), Tashkent 111104, Uzbekistan
  • Jakhangir O. Achilov ORCID Hydrometeorological Research Institute, Tashkent 100052, Uzbekistan
  • Jasur M. Akbarov ORCID Hydrometeorological Research Institute, Tashkent 100052, Uzbekistan
  • Barno K. Akhmuratova ORCID Hydrometeorological Research Institute, Tashkent 100052, Uzbekistan
  • Umid M. Yokubov ORCID Hydrometeorological Research Institute, Tashkent 100052, Uzbekistan

DOI:

https://doi.org/10.30564/re.v8i5.117348

Received:24 August 2026 |Revised:18 September 2026 |Accepted:23 September 2026 |Published Online:21 October 2026

Abstract

Reliable projections of temperature and precipitation underpin water and agricultural planning in arid Central Asia, yet most Coupled Model Intercomparison Project Phase 6 (CMIP6) assessments for the region are domain-averaged and do not resolve the contrasting physical-geographical zones of Uzbekistan. This study assesses projected changes in both variables over the national territory using observation-constrained CMIP6 ensembles under three Shared Socioeconomic Pathway (SSP) scenarios. Historical simulations from 51 models were interpolated to 34 meteorological stations spanning 64 to 2,127 m above sea level (a.s.l.) and ranked against observations for 1991–2014 on a composite score built from absolute bias, error and anomaly correlation. Models ranking in the top ten and offering complete scenario coverage were retained, giving a four-model temperature ensemble and a three-model precipitation ensemble. Bias correction specific to each model, station and month was derived over the baseline and held fixed to 2100. Both ensembles outperform every individual member, with the annual anomaly root mean square error (RMSE) falling to 0.54 °C for temperature and to 24.2% for precipitation. Warming is large, spatially near-uniform and strongly scenario-dependent, reaching +1.34, +2.53 and +4.91 °C for 2071–2100 under the three pathways, with a range across stations of only 0.31 °C under the highest. Precipitation increases by +2.7, +7.2 and +9.5%, but lies well below the 24.2% validation error and the scenario distributions overlap in every decade, so they constrain the direction of the change rather than its magnitude. The increase falls almost entirely in winter and spring and accrues mainly in the mountain zone, and is no relief of water stress in the irrigated lowlands.CMIP6; SSP Scenarios; Multi-Model Ensemble; Bias Correction; Precipitation Anomaly; Temperature Anomaly; Central Asia; Uzbekistan

Keywords:

CMIP6, SSP Scenarios, Multi-Model Ensemble, Bias Correction, Precipitation Anomaly, Temperature Anomaly, Central Asia, Uzbekistan

References

[1] Kholmatjanov, B.M., Petrov, Y.V., Khujanazarov, T., et al., 2020. Analysis of temperature change in Uzbekistan and the regional atmospheric circulation of Middle Asia during 1961–2016. Climate. 8(9), 101. DOI: https://doi.org/10.3390/cli8090101

[2] Fallah, B., Didovets, I., Rostami, M., et al., 2024. Climate change impacts on Central Asia: Trends, extremes and future projections. International Journal of Climatology. 44(10), 3191–3213. DOI: https://doi.org/10.1002/joc.8519

[3] Intergovernmental Panel on Climate Change (IPCC), 2021. Summary for policymakers. In: Masson-Delmotte, V., Zhai, P., Pirani, A., et al. (Eds.). Climate Change 2021—The Physical Science Basis: Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge, UK. pp. 3–32. DOI: https://doi.org/10.1017/9781009157896.001

[4] Lei, X., Xu, C., Liu, F., et al., 2023. Evaluation of CMIP6 models and multi-model ensemble for extreme precipitation over arid Central Asia. Remote Sensing. 15(9), 2376. DOI: https://doi.org/10.3390/rs15092376

[5] Liu, Z., Zhang, G., Ding, J., et al., 2022. Biases of the mean and shape properties in CMIP6 extreme precipitation over Central Asia. Frontiers in Earth Science. 10, 918337. DOI: https://doi.org/10.3389/feart.2022.918337

[6] Ozturk, T., 2023. Projected future changes in extreme climate indices over Central Asia using RegCM4.3.5. Atmosphere. 14(6), 939. DOI: https://doi.org/10.3390/atmos14060939

[7] Nurbatsina, A., Tursunova, A., Makhmudova, L., et al., 2025. Projected hydrological regime shifts in Kazakh rivers under CMIP6 climate scenarios: Integrated modeling and seasonal flow analysis. Atmosphere. 16(9), 1020. DOI: https://doi.org/10.3390/atmos16091020

[8] Salehie, O., Hamed, M.M., Ismail, T., et al., 2023. Selection of CMIP6 GCM with projection of climate over the Amu Darya River Basin. Theoretical and Applied Climatology. 151(3–4), 1185–1203. DOI: https://doi.org/10.1007/s00704-022-04332-w

[9] Gulakhmadov, A., Chen, X., Gulahmadov, N., et al., 2025. 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. Scientific Reports. 15, 3037. DOI: https://doi.org/10.1038/s41598-025-86366-4

[10] Rakhmatova, N., Nishonov, B.E., Kholmatjanov, B.M., et al., 2024. Assessing the potential impacts of climate change on drought in Uzbekistan: Findings from RCP and SSP scenarios. Atmosphere. 15(7), 866. DOI: https://doi.org/10.3390/atmos15070866

[11] Jiang, J., Zhou, T., Chen, X., et al., 2020. Future changes in precipitation over Central Asia based on CMIP6 projections. Environmental Research Letters. 15(5), 054009. DOI: https://doi.org/10.1088/1748-9326/ab7d03

[12] Zhang, J., Wang, F., 2022. Future changes in extreme precipitation in central Asia with 1.5–4 °C global warming based on Coupled Model Intercomparison Project Phase 6 simulations. International Journal of Climatology. 42(16), 8509–8525. DOI: https://doi.org/10.1002/joc.7740

[13] Golian, S., El-Idrysy, H., Stambuk, D., 2023. Using CMIP6 models to assess future climate change effects on mine sites in Kazakhstan. Hydrology. 10(7), 150. DOI: https://doi.org/10.3390/hydrology10070150

[14] Farhat, F., Kashifi, M.T., Jamal, A., et al., 2022. Spatiotemporal projections of precipitation and temperature over Afghanistan based on CMIP6 global climate models. Modeling Earth Systems and Environment. 8(3), 4229–4242. DOI: https://doi.org/10.1007/s40808-022-01361-2

[15] Song, X., Xu, M., Kang, S., et al., 2024. Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models. International Journal of Climatology. 44(14), 5039–5056. DOI: https://doi.org/10.1002/joc.8622

[16] Zhang, Z., Yin, G., Ni, X., et al., 2026. Quantifying historical trends and future projections of evapotranspiration in arid central Asia using selected CMIP6 and GLEAM. Frontiers in Environmental Science. 14, 1757853. DOI: https://doi.org/10.3389/fenvs.2026.1757853

[17] Salehie, O., bin Jamal, M.H., bin Ismail, T., et al., 2024. Projection of future water availability in the Amu Darya Basin. International Journal of Climatology. 44(9), 2948–2961. DOI: https://doi.org/10.1002/joc.8490

[18] Ryu, Y., Chung, E.-S., Seo, S.B., et al., 2020. Projection of potential evapotranspiration for North Korea based on selected GCMs by TOPSIS. KSCE Journal of Civil Engineering. 24(9), 2849–2859. DOI: https://doi.org/10.1007/s12205-020-0283-z

[19] Fotso-Kamga, G., Fotso-Nguemo, T.C., Yepdo, Z.D., et al., 2026. An evaluation of extreme temperature indices as simulated by CMIP6 models over Central Africa. Theoretical and Applied Climatology. 157(3), 182. DOI: https://doi.org/10.1007/s00704-026-06065-6

[20] Enyew, F.B., Sahlu, D., Tarekegn, G.B., et al., 2024. Performance evaluation of CMIP6 climate model projections for precipitation and temperature in the Upper Blue Nile Basin, Ethiopia. Climate. 12(11), 169. DOI: https://doi.org/10.3390/cli12110169

[21] Pinheiro, E., Ouarda, T.B.M.J., 2026. Enhancing machine learning-based seasonal precipitation forecasting using CMIP6 simulations. Atmospheric Research. 329, 108463. DOI: https://doi.org/10.1016/j.atmosres.2025.108463

[22] Arregocés, H.A., Rojano, R., Castellanos, M.L., 2025. Evaluating the enhanced benefits of multi-model ensemble mean from NEX-GDDP-CMIP6 versus native CMIP6 models in accurately representing historical temperature patterns in South America. Environmental Challenges. 21, 101373. DOI: https://doi.org/10.1016/j.envc.2025.101373

[23] Lobanov, V.A., Abannikov, V.N., Okulicheva, A.A., et al., 2023. Modern and future climate change in the Leningrad Oblast and its impact on agroclimatic characteristics. Russian Meteorology and Hydrology. 48(9), 808–817. DOI: https://doi.org/10.3103/S106837392309008X

[24] Wang, S., Sun, M., Yao, X., et al., 2026. Projection of surface air temperature and precipitation in High Mountain Asia and its major upstream river basins through multi-model ensemble from CMIP6. Journal of Mountain Science. 23(8), 3769–3792. DOI: https://doi.org/10.1007/s11629-025-9969-5

[25] Zhang, H., Chapman, S., Trancoso, R., et al., 2024. Assessing the impact of bias correction approaches on climate extremes and the climate change signal. Meteorological Applications. 31(3), e2204. DOI: https://doi.org/10.1002/met.2204

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2026 Vol.8 No.5

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Copyright © 2026 Bakhtiyar M. Kholmatjanov, Ergashali Y. Rakhimov, Jakhangir O. Achilov, Jasur M. Akbarov, Barno K. Akhmuratova, Umid M. Yokubov

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