A global dataset of daily maximum and minimum near-surface air temperature at 1&thinsp;km resolution over land (2003–2020)

Zhang, Tao; Zhou, Yuyu; Zhao, Kaiguang; Zhu, Zhengyuan; Chen, Gang; Hu, Jia; Wang, Li

doi:https://doi.org/10.5194/essd-14-5637-2022

Articles | Volume 14, issue 12

https://doi.org/10.5194/essd-14-5637-2022

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

https://doi.org/10.5194/essd-14-5637-2022

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

Articles | Volume 14, issue 12

Data description paper

|

21 Dec 2022

Data description paper |

| 21 Dec 2022

A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang

Supplement

https://doi.org/10.5194/essd-14-5637-2022-supplement

Data sets

A global dataset of daily maximum and minimum near-surface air temperature at 1-km resolution over land (2003-2020) Tao Zhang and Yuyu Zhou https://doi.org/10.25380/iastate.c.6005185

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Short summary

We generated a global 1 km daily maximum and minimum near-surface air temperature (T_max and T_min) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for T_max and 1.69 to 2.39 °C for T_min. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.