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^′ (∼ 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^′ 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^′ 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^′ 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^′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5^′ 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 https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b) and includes 156 NetCDF files compressed in zip format and one user guidance text file.

The Loess Plateau (LP) in China is sensitive to climate change because of its fragile ecological environment and geographic features. This study presents a detailed assessment of the climate change trends over the LP from 1901 to 2100 based on the 1‐km spatial resolution climate data generated through delta downscaling. The following results are drawn: (1) Delta downscaling performs well in detecting the monthly precipitation and temperature over the LP based on the mean absolute error between downscaled data and independent surface observations. Among the 28 general circulation models, the GFDL‐ESM2M and NorESM1‐M models show the best performance in reproducing the monthly precipitation and temperature in the future period, respectively. (2) The annual precipitation over the entire LP shows no significant trends in the historical and future periods. By contrast, the annual temperature shows a significantly increasing trend with 0.097 °C/10 year in the historical period (1901–2014) and with 0.113, 0.24, 0.355, and 0.558 °C/10 year in the future period (2015–2100) under the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. (3) The significantly increasing trends in precipitation and temperature at each grid of the LP present various spatial distributions in terms of their magnitude. The significant trend magnitude calculated by the downscaled data has a larger range and a higher percentage of area – and is even observed at a small area – compared with that calculated by the raw data. (4) The spatial results calculated by the downscaled data provide more detailed information about the locations and percentages of area, both of which are valuable in assessing the change trends in precipitation and temperature. These spatio‐temporal results present the climate change trends over the LP in detail and provide valuable insights for developing flexible adaptation and mitigation strategies to address the climate change challenges in this region.

Investigating long-term drought trends is of great importance in coping with the adverse effects of global warming. However, little attention has been focused on studying the detailed spatial variability and attribution of drought variation in China. In this study, we first generated a 1 km resolution monthly climate dataset for the period 1901–2100 across China using the delta spatial downscaling method to assess the variability of the Standardized Precipitation Evaporation Index (SPEI). We then developed a simple approach to quantifying the contributions of water supply (precipitation) and demand (potential evapotranspiration, PET) on SPEI variability, according to the meaning of the differentiating SPEI equation. The results indicated that the delta framework could accurately downscale and correct low-spatial-resolution monthly temperatures and precipitation from the Climatic Research Unit and general circulation models (GCMs). Of the 27 GCMs analyzed, the BNU-ESM, CESM1-CAM5, and GFDL-ESM2M were found to be the most accurate in modeling future temperatures and precipitation. We also found that, compared with the past (1901–2017), the climate in the future (2018–2100) will tend toward significant droughts, although both periods showed a high spatial heterogeneity across China. Moreover, the proportion of areas with significantly decreasing SPEI trends was far greater than the proportion of those with increasing trends in most cases, especially for northwestern and northern China. Finally, the proposed approach to quantifying precipitation and PET contributions performed well according to logical evaluations. The percentage contributions of precipitation and PET on SPEI variability varied with study periods, representative concentration pathway scenarios, trend directions, and geographic spaces. In the past, PET contributions for significant downward trends and precipitation contributions for significantly upward trends accounted for 95% and 72%, while their future contributions were 57 ± 22%–149 ± 20% and 95 ± 27%–190 ± 58%, respectively. Overall, our results provide detailed insights for planning flexible adaptation and mitigation strategies to cope with the adverse effects of climate drought across China.