Temperature data is one of our most commonly used meteorological indicators. We have previously shared with you the monthly average temperature grid data and yearly average temperature grid data from 1950 to 2022 with a precision of 0.1° x 0.1° (see previous articles for details)!
This time we are sharing the air temperature grid data with higher precision— the monthly average air temperature grid data with 1km resolution from 1901 to 2022 ! The data comes from the National Qinghai-Tibet Plateau Scientific Data Center, and the data is continuously updated. The data for 2022 was just updated on May 31!
The unit of the monthly average temperature data downloaded from the platform of the National Qinghai-Tibet Plateau Scientific Data Center is 0.1 ℃, and the data format is NETCDF, that is, .nc format. For your convenience, we have performed some processing on the original data, converted the unit into Celsius (°C), and converted the format into raster (.tif) format. In addition, the nationwide data is very large and inconvenient to use. We have divided the national data into provincial data !
The following is a detailed introduction of the data:
01Data preview
nationwide data
First of all, let's take a look at the nationwide data. We will provide three kinds of data:
①Data in original nc format
② data in tif format with a spatial range larger than the national border of China
Let's take the national average temperature in December 2022 as an example to preview. The national scope of the .tif data converted from the original .nc format data is larger than the actual national scope:
③ tif format data within China's borders
We extract the national average temperature data by mask by national boundary:
Data by province
For the data by province, let's take the average temperature of Hubei Province and Jiangsu Province in December 2022 as an example to preview:
02 Data Details
Data Sources:
The data comes from the data shared by Peng Shouzhang on the platform of the National Qinghai-Tibet Plateau Scientific Data Center.
the data shows:
The dataset is explained on the official website. The data is generated by downscaling 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. Moreover, the data of 496 independent meteorological observation points are used for verification, and the verification results are credible. The geographical spatial scope included in this dataset is the main land of the country (including Hong Kong, Macao and Taiwan), excluding areas such as South China Sea islands and reefs. The data coordinate system is recommended to use WGS84.
Data Format:
Raster format (.tif) and NETCDF (.nc) formats
Data unit:
Raster (.tif) format: Celsius (°C)
NETCDF (.nc) format: 0.1 °C
time limit:
1901-2022 (Monthly)
Data coordinates:
for GCS_WGS_1984
Space range:
National/Provincial
Spatial resolution:
0.0083333° (about 1km)
Data reference:
Peng Shouzhang. (2019). China's 1km resolution monthly average temperature dataset (1901-2022). National Qinghai-Tibet Plateau Scientific Data Center.
Peng, S. (2019). 1-km monthly mean temperature dataset for china (1901-2022). National Tibetan Plateau Data Center.
https://doi.org/10.11888/Meteoro.tpdc.270961. https://cstr.cn/18406.11.Meteoro.tpdc.270961.
Article citation:
1.Peng, S. Z, 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.
2.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
3.Ding, Y.X., & Peng, S.Z. (2020). Spatiotemporal trends and attribution of drought across China from 1901–2100. Sustainability, 12(2), 477.
4.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
If you have data usage needs, please refer to it according to the requirements of the official platform. For more data details, you can check the official website to learn!
At the bottom of the article is our official account business card. We will regularly introduce various types of urban data and data visualization and analysis technologies. Regarding the 1km resolution monthly average temperature grid data from 1901 to 2022, you are welcome to pay more attention to us to learn more!