AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP026100057
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ORIGINAL RESEARCH ARTICLE

Adaptive bias correction for subseasonal temperature forecasting in East Asia

Hao Wu1* ,  Haonan Ji2
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1 School of Automation, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
2 Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Wuxi University, Wuxi, Jiangsu, China
AJWEP, 026100057 https://doi.org/10.36922/AJWEP026100057
Received: 3 March 2026 | Revised: 28 April 2026 | Accepted: 30 April 2026 | Published online: 21 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Subseasonal (2–6-week) temperature prediction is important for climate-risk management, energy dispatch, and early warning of high-impact weather over East Asia. However, operational dynamical forecast systems often exhibit substantial lead-dependent biases in this region. In this study, we evaluate an adaptive bias correction (ABC) framework for East Asian subseasonal 2-m temperature forecasts based on Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecasts data, and compare its performance with raw forecasts and conventional debiasing. Results show that ABC consistently reduced forecast errors and improved spatial consistency across lead times, with particularly clear benefits during winter cold-surge conditions and summer high-temperature periods. The improvements arise from the complementary integration of climatological, dynamical, and persistence-based information, which helps address the spatiotemporal heterogeneity of forecast errors. These results suggest that adaptive post-processing offers a practical approach to improving operational subseasonal temperature prediction over East Asia.

Graphical abstract
Keywords
Machine learning
Subseasonal-to-seasonal prediction
Temperature forecasting
East Asia
Root mean square error
Bias correction
Funding
This study was jointly funded by China State Railway Group Science and Technology Research and Development Program (N2024T008), Guangdong Basic and Applied Basic Research Foundation.
Conflict of interest
The authors declare they have no competing interests.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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