Articles | Volume 26, issue 9
https://doi.org/10.5194/nhess-26-4407-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-4407-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Sep 2026
Research article |
| 14 Sep 2026
| 14 Sep 2026
Short-term drought forecasting in Iran using multi-source machine learning: an assessment of autoregressive, teleconnection-driven, and hybrid paradigms
Navigation College, Dalian Maritime University, Dalian, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Department of Physical Geography, Faculty of geography and environmental planning, University of Sistan and Baluchestan, Zahedan, Iran
Pouria Jafari
Department of Electronic and Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran
Alireza Ghaemi
Department of Physical Geography, Faculty of geography and environmental planning, University of Sistan and Baluchestan, Zahedan, Iran
Jing Yang
Faculty of Geographical Science, Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing, China
Fatemeh Firoozi
Department of Humanities and Social Science, Farhangyan University, Tehran, Iran
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
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Short summary
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The configuration and simulated performance of tropical cyclones (TCs) in FGOALS-f3-L/H will be introduced firstly. The results indicate that the simulated performance of TC activities is improved globally with the increased horizontal resolution especially in TC counts, seasonal cycle, interannual variabilities and intensity aspects. It is worth establishing a high-resolution coupled dynamic prediction system based on FGOALS-f3-H (~ 25 km) to improve the prediction skill of TCs.
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Short summary
To improve drought early warnings, we tested if past drought patterns, global climate signals, or their combination best predict future droughts across Iran. Using thirty years of rainfall data and nine computer models, we found no single approach works everywhere. Combined methods excel in dry areas, while single methods suit coasts. This proves we must abandon uniform tools and build customized, location-specific forecasting systems to better protect communities from climate hazards.
To improve drought early warnings, we tested if past drought patterns, global climate signals,...
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