Zoom in

This dataset is the monthly precipitation data of China, with a spatial resolution of 0.0083333 ° (about 1km) and a time range of 1901.1-2021.12. The data format is NETCDF, i.e.. Nc format. This dataset is generated in China through the Delta spatial downscaling scheme based on the global 0.5 ° climate dataset released by CRU and the global high-resolution climate dataset released by WorldClim. In addition, 496 independent meteorological observation point data are used for verification, and the verification results are reliable. This data set covers the main land areas in China (including Hong Kong, Macao and Taiwan), excluding islands and reefs in the South China Sea. In order to facilitate storage, the data are all int16 type and stored in nc files, with precipitation units of 0.1mm.

NC data can be mapped using ArcMAP software; Matlab software can also be used for extraction processing. Matlab has released the function to read and store nc files. The read function is ncread, and switch to the nc file storage folder. The statement is expressed as: ncread ('XXX.nc ',' var ', [i j t], [leni lenj lent]), where XXX.nc is the file name, and is the string required' '; Var is from XXX The variable name read in NC. If it is a string, '' is required; i. J and t are the starting row, column and time of the read data respectively, and leni, lenj and lent i are the length of the read data in the row, column and time dimensions respectively. In this way, this function can be used to read in any region and any time period in the study area. There are many commands about NC data in the help of Matlab, which can be viewed. WGS84 is recommended for data coordinate system.

File naming and required software

2019qzkk0603-zgyjsl: in which, 2019QZKK is the project number, 06 represents task 6,03 represents topic 3, and zgyjsl is the initial letter of Chinese pinyin;The data format is NETCDF, nc data can be opened by ArcMAP software;Matlab can be used for extraction and processing, Matlab released the function to read and store the nc file, the reading function is ncread, switch to the nc file storage folder, the statement is expressed as: ncread (' XXX. Nc ', 'var', [I j t], [leni lenj lenj]), where XXX.Var is the name of the variable read from XXX. Nc.I, j and t are respectively the beginning row, column and time of data reading, while leni, lenj and lent I are respectively the length of data reading in the dimension of row, column and time.Thus, this function can be used to read any region and any time period in the study area.

Data Citations Data citation guideline What's data citation?

Peng, S. (2020). 1-km monthly precipitation dataset for China (1901-2021). A Big Earth Data Platform for Three Poles, DOI: 10.5281/zenodo.3185722. (Download the reference： RIS | Bibtex )

1. Peng, S.Z., Ding, Y.X., Wen, Z.M., Chen, Y.M., Cao, Y., & Ren, J.Y. (2017). Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011-2100. Agricultural and Forest Meteorology, 233, 183-194. https://doi.org/10.1016/j.agrformet.2016.11.129( View Details | Bibtex)

2. Ding, Y.X., & Peng, S.Z. (2020). Spatiotemporal trends and attribution of drought across China from 1901–2100. Sustainability, 12(2), 477.( View Details | Bibtex)

3. Peng, S.Z., Ding, Y.X., Liu, W.Z., & Li, Z. (2019). 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 11, 1931–1946. https://doi.org/10.5194/essd-11-1931-2019( View Details | Bibtex)

4. Peng, S. , Gang, C. , Cao, Y. , & Chen, Y. . (2017). Assessment of climate change trends over the loess plateau in china from 1901 to 2100. International Journal of Climatology.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Second Tibetan Plateau Scientific Expedition Program

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

Related Resources

Comments

Current page automatically show English comments Show comments in all languages