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

DOI

Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km and over a long-term period (1961 to 2019). It has been named the HRLT. The daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin plate splines. It is based on the 0.5° × 0.5° grid dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using meteorological station observation data. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R^2), and Nash-Sutcliffe modeling efficiency (NSE) were 1.07 ℃, 1.62 ℃, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R^2, and NSE were 1.08 ℃, 1.53 ℃, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R^2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of the other two existing datasets and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution and over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy, is suitable for future environmental analyses, especially the effects of extreme weather.

The datasets are stored in NetCDF format. The file name is China_1km_keyword_startyear_endyear.nc, where keyword is variable (maxtmp for maximum temperature, mintmp for minimum temperature, and prep for precipitation); startyear represents the start year and endyear represents the end year.The files in this dataset can be opened and viewed with the HDFView program, and the netCDF4 module in Python can read and output data.

Identifier
DOI https://doi.org/10.1594/PANGAEA.940192
Related Identifier https://doi.org/10.1594/PANGAEA.941329
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.940192
Provenance
Creator Qin, Rongzhu; Feng, Zhang
Publisher PANGAEA
Publication Year 2022
Rights Licensing unknown: Please contact principal investigator/authors to gain access and request licensing terms; Data access is restricted (moratorium, sensitive data, license constraints)
OpenAccess false
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 177 data points
Discipline Earth System Research