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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (10): 1667-1692    DOI: 10.1016/j.jaridl.2026.10.001     CSTR: 32276.14.JAL.20250499
Research article     
Evaluation of long-term climate dynamic variations across Uzbekistan using multi-source datasets
ZHUO Ruina1, ZHANG Xian2, ZHANG Zhuo3,4,5, ZHENG Xiaowei6, NI Xiaohua1, Nazrullozoda SULAIMON7, ZHANG Qianqian1,8, HU Zengyun()
1 School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
2 School of Public Health, Zhengzhou University, Zhengzhou 450001, China
3 State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
4 Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
5 University of Chinese Academy of Sciences, Beijing 100049, China
6 School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
7 Institute of Veterinary Medicine of the Tajik Academy of Agricultural Sciences, Dushanbe 734003, Tajikistan
8 Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077, China
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Abstract  

Uzbekistan is a core country of the Silk Road Economic Belt; thus, its long-term climate variations play key roles in achieving the objectives set by Sustainable Development Goal 13 (SDG-13) to address climate change and its impacts. This study utilized temperature and precipitation data from 32 meteorological stations across Uzbekistan (1950-2023), Climatic Research Unit Time-Series (CRU-TS) version 4.08 (1901-2023), European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) (1950-2022), and Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) (1980-2024) to thoroughly investigate the spatiotemporal characteristics of temperature and precipitation in Uzbekistan from 1901 to 2024. The Distance between Indices of Simulation and Observation (DISO) metric was employed to evaluate the accuracy of the gridded datasets, and the Mann-Kendall method was implemented to analyze the linear trends of temperature and precipitation. Results indicated that ERA5 temperature data are the most consistent with the observed temperature data, whereas CRU-TS data perform best in determining long-term precipitation variability. The century-scale trend represented by the CRU-TS data showed significant warming at a rate of nearly 0.17°C/10a, exceeding the Northern Hemisphere average. Uzbekistan showed no clear upward trend in precipitation during 1901-2023. Rapid warming and heightened interannual precipitation variability were observed in ERA5 (1950-2022) and MERRA2 (1980-2024) over their respective coverage periods. CRU-TS, ERA5, and MERRA2 data all revealed a shift of the warming center from the Aral Sea Basin to the central and southeastern regions of Uzbekistan. Nevertheless, the Aral Sea Basin remained in the core warming zone, with CRU-TS data indicating a centennial warming rate of 0.16°C/10a-0.20°C/10a. The precipitation patterns revealed a complex "wetter southeast and drier central-west" and "wetter summer and drier autumn-winter" dichotomy. These findings could enhance the understanding of long-term climate change in Uzbekistan and provide a scientific foundation for implementing SDG-13 to mitigate the impacts of climate change in the region.

Key words： Climatic Research Unit Time-Series (CRU-TS)      European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5)      Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2)      Distance between Indices of Simulation and Observation (DISO)      climate change      Central Asia     
Received: 08 October 2025      Published: 31 October 2026
Corresponding Authors: *HU Zengyun (E-mail: huzengyun@ms.xjb.ac.cn)
About author: First author contact:

Conceptualization: ZHUO Ruina, ZHANG Qianqian; Methodology: ZHUO Ruina, ZHANG Qianqian, HU Zengyun; Formal analysis: ZHUO Ruina; Visualization: ZHANG Zhuo, ZHANG Xian; Writing - original draft preparation: ZHUO Ruina, NI Xiaohua; Writing - review and editing: ZHUO Ruina, ZHANG Qianqian, ZHENG Xiaowei; Funding acquisition: HU Zengyun; Resources: Nazrullozoda SULAIMON; Supervision: ZHANG Qianqian, HU Zengyun. All authors approved the manuscript.
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ZHUO Ruina
ZHANG Xian
ZHANG Zhuo
ZHENG Xiaowei
NI Xiaohua
Nazrullozoda SULAIMON
ZHANG Qianqian
HU Zengyun
Cite this article:

ZHUO Ruina, ZHANG Xian, ZHANG Zhuo, ZHENG Xiaowei, NI Xiaohua, Nazrullozoda SULAIMON, ZHANG Qianqian, HU Zengyun. Evaluation of long-term climate dynamic variations across Uzbekistan using multi-source datasets. Journal of Arid Land, 2026, 18(10): 1667-1692.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.10.001     OR     http://jal.xjegi.com/Y2026/V18/I10/1667

Fig. 1 Overview of the study area based on the elevation data and spatial distribution of meteorological stations
Fig. 2 Technical workflow for analyzing long-term climate dynamic variations in Uzbekistan using multiple climate datasets. CRU-TS, Climatic Research Unit Time-Series; ERA5, European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation; MERRA2, Modern-Era Retrospective analysis for Research and Applications, version 2; CC, correlation coefficient; MAE, mean absolute error; RMSE, root mean square error; DISO, Distance Index between Simulation and Observation; LAI, leaf area index.
Fig. 3 Temporal variation in annual mean temperature for five stations in Uzbekistan during 1950-2017. (a), Cimbaj; (b), Fergana; (c), Tashkent; (d), Samarkand; (e), Tamdy.
Fig. 4 Temporal variation in seasonal mean temperature for five stations in Uzbekistan during 1950-2017. (a), Cimbaj; (b), Fergana; (c), Tashkent; (d), Samarkand; (e), Tamdy.
Station Season Linear fitting equation P R2 Mean (°C) SD (°C) CV (%)
Cimbaj Spring y=0.050x-85.9 <0.001 0.36 13.03 1.64 12.59
Summer y=0.045x-63.6 <0.001 0.68 25.98 1.08 4.17
Autumn y=0.027x-43.0 <0.001 0.16 11.13 1.33 11.98
Winter y=0.027x-55.9 0.090 0.04 -3.29 2.53 -76.91
Fergana Spring y=0.018x-19.3 0.033 0.08 15.31 1.00 6.55
Summer y=0.028x-28.5 <0.001 0.38 25.94 0.73 2.82
Autumn y=0.035x-55.1 <0.001 0.20 13.95 1.28 9.15
Winter y=0.048x-94.1 0.003 0.15 1.46 2.05 139.86
Tashkent Spring y=0.034x-51.6 <0.001 0.29 15.29 1.24 8.14
Summer y=0.028x-30.0 <0.001 0.42 26.32 0.86 3.28
Autumn y=0.037x-58.3 <0.001 0.33 14.64 1.26 8.63
Winter y=0.037x-71.1 0.008 0.10 3.20 2.33 73.02
Samarkand Spring y=0.025x-34.8 0.008 0.13 14.17 1.14 8.07
Summer y=0.036x-46.9 <0.001 0.49 24.78 0.84 3.40
Autumn y=0.053x-90.9 <0.001 0.44 13.99 1.30 9.30
Winter y=0.037x-69.8 0.043 0.07 3.11 2.21 71.16
Tamdy Spring y=0.040x-64.7 <0.001 0.28 15.53 1.51 9.75
Summer y=0.038x-47.0 <0.001 0.45 28.93 1.13 3.91
Autumn y=0.034x-53.9 <0.001 0.20 13.82 1.50 10.86
Winter y=0.018x-35.1 0.288 0.02 -0.17 2.66 -
Table 1 Seasonal mean temperature trends at five stations in Uzbekistan
Fig. 5 Temporal variation in annual precipitation at ten stations in Uzbekistan during 1950-1976. (a), Cimbaj; (b), Nukus; (c), Urgench; (d), Nurata; (e), Samarkand; (f), Shirabad; (g), Tashkent; (h), Andizan; (i), Fergana; (j), Tamdy.
Fig. 6 Temporal variation in annual precipitation at ten stations in Uzbekistan during 1985-2006. (a), Cimbaj; (b), Nukus; (c), Urgench; (d), Nurata; (e), Samarkand; (f), Shirabad; (g), Tashkent; (h), Andizan; (i), Fergana; (j), Tamdy.
Station Season Mean (mm) Slope (mm/a) P Station Season Mean (mm) Slope (mm/a) P
Cimbaj Spring 51.59 0.56 0.043 Tashkent Spring 170.92 0.03 0.959
Summer 11.08 0.11 0.216 Summer 3.10 0.15 0.940
Autumn 21.92 0.25 0.047 Autumn 17.49 0.16 0.685
Winter 32.80 0.18 0.169 Winter 85.30 0.90 0.010
Nukus Spring 39.69 0.25 0.287 Andizan Spring 87.60 -0.24 0.546
Summer 8.32 -0.05 0.511 Summer 17.55 -0.01 0.346
Autumn 19.16 0.21 0.192 Autumn 79.55 -0.13 0.528
Winter 30.97 0.19 0.226 Winter 165.21 0.93 0.680
Urgench Spring 50.22 1.07 <0.001 Fergana Spring 65.55 0.29 0.196
Summer 11.47 0.28 0.006 Summer 20.37 -0.16 0.383
Autumn 24.63 0.98 0.001 Autumn 54.72 -0.28 0.700
Winter 48.53 1.68 <0.001 Winter 88.10 0.15 0.198
Nurata Spring 113.16 0.70 0.520 Tamdy Spring 53.44 -0.17 0.488
Summer 6.22 0.11 0.502 Summer 16.99 0.10 0.504
Autumn 32.37 0.40 0.354 Autumn 38.52 0.10 0.490
Winter 84.75 0.45 0.408 Winter 58.92 0.28 0.372
Samarkand Spring 170.22 -0.14 0.803 Shirabad Spring 87.11 0.48 0.193
Summer 9.59 0.02 0.772 Summer 8.22 0.06 0.053
Autumn 55.42 0.07 0.806 Autumn 17.51 0.07 0.311
Winter 128.45 0.46 0.111 Winter 41.74 0.14 0.011
Table 2 Seasonal precipitation trends at ten stations in Uzbekistan during 1950-2023
Fig. 7 Comparisons of CRU-TS (a), ERA5 (b), and MERRA2 (c) with station observations for annual mean temperature
Fig. 8 Comparisons of CRU-TS (a), ERA5 (b), and MERRA2 (c) with station observations for annual precipitation
Fig. 9 Temporal variations in annual mean temperature (a) and seasonal mean temperatures (b-e) in Uzbekistan based on CRU-TS, ERA5, and MERRA2 datasets
Fig. 10 Spatial distributions of multi-year average temperature and annual mean temperature trends based on CRU-TS (a and b), ERA5 (c and d), and MERRA2 (e and f) datasets. Black dots indicate trends significant at the P<0.050 level.
Fig. 11 Spatial distribution of seasonal mean temperature trends based on CRU-TS (a-d), ERA5 (e-h), and MERRA2 (i-l) datasets. Black dots indicate trends significant at the P<0.050 level.
Fig. 12 Temporal variations in annual precipitation (a) and seasonal precipitation (b-e) in Uzbekistan based on CRU-TS, ERA5, and MERRA2 datasets
Fig. 13 Spatial distributions of multi-year average precipitation and annual precipitation trends based on CRU-TS (a and b), ERA5 (c and d), and MERRA2 (e and f) datasets. Black dots indicate trends significant at the P<0.050 level.
Fig. 14 Spatial distribution of seasonal precipitation trends based on CRU-TS (a-d), ERA5 (e-h), and MERRA2 (i-l) datasets. Black dots indicate trends significant at the P<0.050 level.
Fig. 15 Temporal variations in annual mean temperature (a) and seasonal mean temperatures (b-e) based on the CRU-TS dataset for Uzbekistan, Central Asia, and the Northern Hemisphere from 1901 to 2023
Fig. 16 Temporal variations in annual precipitation (a) and seasonal precipitation (b-e) based on the CRU-TS dataset for Uzbekistan, Central Asia, and the Northern Hemisphere from 1901 to 2023
Fig. 17 Temporal variations in annual LAI (a) and seasonal LAI (b) in Uzbekistan during 1982-2021
Fig. 18 Correlation matrix of LAI, precipitation (PRE), and temperature (TEM) at annual and seasonal scales in Uzbekistan during 1982-2021. *, P<0.050 level; **, P<0.010 level; ***, P<0.001 level.
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