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https://doi.org/10.5194/essd-2019-83
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-83
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Jun 2019

18 Jun 2019

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This preprint has been withdrawn by the authors.

High-spatial-resolution monthly temperature and precipitation dataset for China for 1901–2017

Shouzhang Peng1, Yongxia Ding2, and Zhi Li3 Shouzhang Peng et al.
  • 1State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, 712100, China
  • 2School of Geography and Tourism, Shaanxi Normal University, Xi’an, 710169, China
  • 3College of Natural Resources and Environment, Northwest A&F University, Yangling,712100, China

Abstract. High-spatial-resolution and long-term climate data are highly desirable for understanding climate-related natural processes. China covers a large area with a low density of weather stations in some regions, especially mountainous regions. This study describes a high-spatial-resolution (0.5’, ∼1 km) dataset of monthly temperatures (minimum, maximum, and mean TMPs) and precipitation (PRE) for the main land area of China for the period 1901–2017. The dataset was spatially downscaled from raw 30’ climatic research unit (CRU) time series data and validated using data from 745 weather stations across China. Compared to raw CRU data of low spatial resolution, the mean absolute error decreased by 0.56 °C for the TMPs and 10.1 % for PRE, the root-mean-square error decreased by 0.65 °C for the TMPs and 11.6 % for PRE, and the Nash–Sutcliffe efficiency coefficients increased from 0.83 to 0.95 for the TMPs and from 0.63 to 0.76 for PRE. Indirect validations from site-scale observations indicated that the dataset captured the climatology well, as well as the annual and seasonal monotonic trends in each climatic variable considered. We concluded that the new high-spatial-resolution dataset is sufficiently reliable for use in investigation of climate change across China. This dataset will be useful in investigations related to climate change across China. The dataset presented in this article is published in the Network Common Data Form (NetCDF) at https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and https://doi.org/10.5281/zenodo.3185722 for temperatures (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file.

This preprint has been withdrawn.

Shouzhang Peng et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Shouzhang Peng et al.

Data sets

High-spatial-resolution monthly precipitation dataset over China during 1901–2017 S. Peng https://doi.org/10.5281/zenodo.3114194

High-spatial-resolution monthly temperatures dataset over China during 1901–2017 S. Peng https://doi.org/10.5281/zenodo.3185722

Shouzhang Peng et al.

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Short summary
This study describes a 1-km monthly minimum, maximum, and mean temperatures and precipitation dataset for the main land area of China during 1901–2017. It is the first dataset developed with such a high spatiotemporal resolution over such a long time period for China. The dataset was evaluated by the observations during 1951–2016 using 745 national weather stations, and the evaluation indicated the dataset is sufficiently reliable for use in investigation of climate change across China.
This study describes a 1-km monthly minimum, maximum, and mean temperatures and precipitation...
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