A U-Net-Based Temperature Bias Correction Method for the REMO2015 Regional Climate Model in CORDEX-EA

Shibin Zheng, Chenwei Shen, Bin Li

2025

Abstract

Regional climate models suffer from insufficient resolution and deficiencies in their dynamic processes, leading to systematic biases in surface air temperature simulations that require correction. In this research, a deep learning bias correction model, CE-MS-Unet, is proposed. This model incorporates multi-scale residual blocks and calendar month data to improve surface air temperature simulations of the REMO2015 regional climate model during the second phase of the Coordinated Regional Downscaling Experiment East Asia (CORDEX-EA-II) over mainland China. Experimental results indicate that, compared to Linear Scaling, Quantile Delta Mapping, and the deep learning model CU-net, CE-MS-Unet performs better in correcting climate averages and seasonal cycles, resulting in corrected data with greater overall agreement and improved spatial correlation. It effectively reduces biases and provides more accurate climate predictions. This study offers new insights and methods to improve the bias correction of temperature in regional climate models.

Paper Citation

in Harvard Style

Zheng S., Shen C. and Li B. (2025). A U-Net-Based Temperature Bias Correction Method for the REMO2015 Regional Climate Model in CORDEX-EA. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 563-570. DOI: 10.5220/0013104200003905

in Bibtex Style

@conference{icpram25,
author={Shibin Zheng and Chenwei Shen and Bin Li},
title={A U-Net-Based Temperature Bias Correction Method for the REMO2015 Regional Climate Model in CORDEX-EA},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={563-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013104200003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A U-Net-Based Temperature Bias Correction Method for the REMO2015 Regional Climate Model in CORDEX-EA
SN - 978-989-758-730-6
AU - Zheng S.
AU - Shen C.
AU - Li B.
PY - 2025
SP - 563
EP - 570
DO - 10.5220/0013104200003905
PB - SciTePress