HRLT: a high-resolution (1&thinsp;d, 1&thinsp;km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China

Qin, Rongzhu; Zhao, Zeyu; Xu, Jia; Ye, Jian-Sheng; Li, Feng-Min; Zhang, Feng

doi:https://doi.org/10.5194/essd-14-4793-2022

Articles | Volume 14, issue 11

https://doi.org/10.5194/essd-14-4793-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-4793-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 11

Data description paper

|

02 Nov 2022

Data description paper |

| 02 Nov 2022

HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China

Rongzhu Qin, Zeyu Zhao, Jia Xu, Jian-Sheng Ye, Feng-Min Li, and Feng Zhang

Data sets

HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China R. Qin and F. Zhang https://doi.org/10.1594/PANGAEA.941329

Model code and software

Interpolation of noisy multi-variate data using machine learning ensembling J. L. Brown https://github.com/jasonleebrown/machisplin

Short summary

This work presents a new high-resolution daily gridded maximum temperature, minimum temperature, and precipitation dataset for China (HRLT) with a spatial resolution of 1 × 1 km for the period 1961 to 2019. This dataset is valuable for crop modelers and climate change studies. We created the HRLT dataset using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin-plate splines.