Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 14, issue 7
Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3273-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3273-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 7
Data description paper
 | 
15 Jul 2022
Data description paper |  | 15 Jul 2022

GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning

Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang

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Short summary
We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to the interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing data sets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature data set with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
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