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arXiv · 2511.08964

A high-resolution prediction dataset for solar energy across China (2015-2060)

Daoming Zhu·Xinghong Cheng·Yanbo Shen·Chunsong Lu·Duanyang Liu·Shuqi Yan·Naifu Shao·Zhongfeng Xu·Jida Peng·Bing Chen
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Abstract

A high spatiotemporal resolution and accurate middle-to-long-term prediction data is essential to support China's dual-carbon targets under global warming scenarios. In this study, we simulated hourly solar radiation at a 10 km* 10 km resolution in January, April, July, and October at five-year intervals from 2015 to 2060 across China using the WRF-Chem model driven by bias-corrected CMIP datasets and future emission inventories. We further calculated the monthly photovoltaic power potentials based on an improved assessment model. Results indicate that the WRF-Chem model can reproduce the spatiotemporal evolution of solar radiation with small simulation errors. GHI in 2030 and 2060 over China are characterized by a pronounced west-to-east gradient. The interannual fluctuations of GHI from 2015 to 2060 over China's major PV power generation bases are small, and the interannual variability of GHI is mainly dominated by TCC and the influence of AOD is limited. National averaged PV power generation in China shows a significant growth trend and increases from 68.7 TWh in 2015 to 129.7 TWh in 2060, which is approximately twice the 2015 value. The dataset will provide an important scientific basis for renewable energy planning and grid security under China's dual-carbon strategy.

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Daoming Zhu, Xinghong Cheng, Yanbo Shen, Chunsong Lu, Duanyang Liu, Shuqi Yan, Naifu Shao, Zhongfeng Xu, Jida Peng, Bing Chen. 2025-11-12. A high-resolution prediction dataset for solar energy across China (2015-2060). https://arxiv.org/abs/2511.08964

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