Downscaling daily climate data with super-resolution and CMIP6 global climate models to predict the climate in China
文献类型：期刊论文
| 作者 | Zhang, Fu-Yao1,2; Liu, Yun-Xi1,2; Li, Xiu-Bin1,2; Wang, Xue1; Tan, Ming-Hong1,2; Xin, Liang-Jie2 |
| 刊名 | ADVANCES IN CLIMATE CHANGE RESEARCH
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 17期号:1页码:79-90 |
| 关键词 | CMIP6 Super-resolution models Downscaling Deep learning |
| ISSN号 | 1674-9278 |
| DOI | 10.1016/j.accre.2025.11.007 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Traditional downscaling methods are often hampered by high computational costs and assumptions of statistical stationarity, which compromise the reliability of future regional climate risk assessments. To address these problems, we developed a downscaling framework that integrates super-resolution (SR) deep learning from computer vision into climate science by systematically comparing and evaluating eight distinct SR architectures. The optimal model was identified through a comparative evaluation of these super-resolution models, and the GCM climate data from CMCC-ESM2 and NorESM2-MM were downscaled to 10 km across China. The residual channel attention network (RCAN) model outperformed the other models in downscaling the four selected climate variables (tas, pr, rsds and sfcWind). The RCAN model achieved the highest R 2 values for tas (0.996), pr (0.906), rsds (0.992) and sfcWind (0.965), along with the lowest mean absolute errors (MAEs) for tas (0.428 K), pr (0.540 mm), rsds (3.329 W/m 2 ) and sfcWind (0.172 m/s). In addition, the downscaling performance achieved with RCAN had minimal errors and stable results at annual, seasonal and monthly scales. Seasonal R 2 values for tas consistently exceeded 0.994, and the monthly MAE for sfcWind showed a minimal fluctuation amplitude of only 0.025 m/s throughout the year. Seasonal fluctuations were noted in rsds and pr during summer, whereas tas and sfcWind errors remained stable throughout the year. Future climate projections for 2015-2100 based on downscaled GCMs show marked warming trends, with temperature increases of up to 0.83 K per decade under SSP-585 and precipitation increasing by up to 23.46 mm per decade. Solar radiation trends vary by scenario and region, whereas wind speeds decline consistently across all scenarios. This framework, together with the optimally selected RCAN model, demonstrates a strong capability to generate high-fidelity 10-km climate data that effectively capture local topographic and climatic features. Such an enhancement substantially improves the precision of regional climate change assessments, providing a more robust scientific foundation for developing targeted adaptation strategies and supporting informed decision-making. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001719773300001 |
| 出版者 | KEAI PUBLISHING LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221329]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Wang, Xue |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Fu-Yao,Liu, Yun-Xi,Li, Xiu-Bin,et al. Downscaling daily climate data with super-resolution and CMIP6 global climate models to predict the climate in China[J]. ADVANCES IN CLIMATE CHANGE RESEARCH,2026,17(1):79-90. |
| APA | Zhang, Fu-Yao,Liu, Yun-Xi,Li, Xiu-Bin,Wang, Xue,Tan, Ming-Hong,&Xin, Liang-Jie.(2026).Downscaling daily climate data with super-resolution and CMIP6 global climate models to predict the climate in China.ADVANCES IN CLIMATE CHANGE RESEARCH,17(1),79-90. |
| MLA | Zhang, Fu-Yao,et al."Downscaling daily climate data with super-resolution and CMIP6 global climate models to predict the climate in China".ADVANCES IN CLIMATE CHANGE RESEARCH 17.1(2026):79-90. |
入库方式： OAI收割
来源：地理科学与资源研究所
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