1thinsp;km monthly temperature and precipitation dataset for China from 1901 to 2017

• Published in: Earth system science data Vol. 11; no. 4; pp. 1931 - 3861
• Main Authors: Peng, Shouzhang, Ding, Yongxia, Liu, Wenzhao, Li, Zhi
• Format: Journal Article
• Language: English
• Published: Copernicus GmbH 13.12.2019
• Subjects: Climatic changes; China
• DOI: 10.5194/essd-11-1931-2019
• ISSN: 1866-3508

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 (e.g., mountainous) regions. This study describes a 0.5.sup.' (â¼ 1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs) and precipitation (PRE) for China in the period of 1901-2017. The dataset was spatially downscaled from the 30.sup.' Climatic Research Unit (CRU) time series dataset with the climatology dataset of WorldClim using delta spatial downscaling and evaluated using observations collected in 1951-2016 by 496 weather stations across China. Prior to downscaling, we evaluated the performances of the WorldClim data with different spatial resolutions and the 30.sup.' original CRU dataset using the observations, revealing that their qualities were overall satisfactory. Specifically, WorldClim data exhibited better performance at higher spatial resolution, while the 30.sup.' original CRU dataset had low biases and high performances. Bicubic, bilinear, and nearest-neighbor interpolation methods employed in downscaling processes were compared, and bilinear interpolation was found to exhibit the best performance to generate the downscaled dataset. Compared with the evaluations of the 30.sup.' original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5.sup.' dataset downscaled by bilinear interpolation) decreased by 35.4 %-48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %-44.9 % for TMPs and by 25.8 % for PRE. The Nash-Sutcliffe efficiency coefficients increased by 9.6 %-13.8 % for TMPs and by 31.6 % for PRE, and correlation coefficients increased by 0.2 %-0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new dataset in the period of 1901-2017 depended on the quality of the original CRU and WorldClim datasets. Therefore, the new dataset was reliable, as the downscaling procedure further improved the quality and spatial resolution of the CRU dataset and was concluded to be useful for investigations related to climate change across China. The dataset presented in this article has been published in the Network Common Data Form (NetCDF) at