Abstract. 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.

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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 regions, especially in mountainous regions. 10This study describes a 0.5' (~1 km) dataset of monthly air temperatures at 2 m (minimum, maximum, and mean TMPs) and precipitation (PRE) for China from 1901-2017. The dataset was spatially downscaled from 30' climatic research unit (CRU) time series dataset with the climatology dataset of WorldClim by using Delta spatial downscaling and evaluated using observations during 1951-2016 from 496 weather stations across China. Moreover, the bicubic, bilinear, and nearest-neighbor interpolation methods were compared in the downscaling processes. Among the three interpolation methods, bilinear 15 interpolation exhibited the best performance to generate the downscaled dataset. Compared with the evaluations of the original CRU dataset, the mean absolute error of the new dataset (i.e., 0.5' downscaled dataset with the bilinear interpolation) relatively decreased by 35.4 %-48.7 % for TMPs and 25.7 % for PRE, the root-mean-square error relatively decreased by 32.4 %-44.9 % for TMPs and 25.8 % for PRE, the Nash-Sutcliffe efficiency coefficients relatively increased by 9.6 %-13.8 % for TMPs and 31.6 % for PRE, and the correlation coefficients relatively increased by 0.2 %-0.4 % for TMPs and 5.0 % for PRE. Further, 20the new dataset could provide detailed climatology data and annual trend of each climatic variable across China, and the results could be well evaluated using observations at the station. Although the evaluation of new dataset was not carried out before 1950 owing to a lack of data availability, the downscaling procedure used data from CRU and WordClim and did not incorporate observations. Thus the quality of the new dataset before 1950 mainly depended on that of the CRU and WordClim datasets. The evaluations showed that the overall quality of the CRU and WordClim datasets was satisfactory, and the 25 downscaling procedure further improved the quality and spatial resolution of the CRU dataset. The new dataset will be useful in investigations related to climate change across China. The dataset presented in this article has been published in Network Common Data Form (NetCDF) at http://doi.org/10.5281/zenodo.3114194 for precipitation (Peng, 2019a) and http://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m (Peng, 2019b). The dataset includes 156 NetCDF files compressed with zip format and one user guidance text file. 30

a b s t r a c tSmoothed particle hydrodynamics (SPH), as a Lagrangian meshfree particle method, has been applied to modeling viscous liquid drop with surface tension and wetting dynamics. In the SPH model, the van der Waals (vdW) equation of state is usually used to describe the gas-to-liquid phase transition similar to that of a real fluid. However, the attractive forces between SPH particles originated from the cohesive pressure of the vdW equation of state can lead to tensile instability, which is associated with unphysical phenomena such as particle clustering or blowing away. This paper presents an improved SPH method for modeling viscous liquid drop. The inherent tensile instability in SPH is removed by using a hyperbolicshaped kernel function which possesses non-negative second derivatives. A single-step approximation for heat flux is used in modeling viscous liquid drop with smoother temperature field. The formations of viscous liquid drops, both in 2D and 3D, are tested and it clearly demonstrates that the tensile instability can be effectively removed. The improved SPH method is also used to model two other numerical examples including the oscillation and binary collision of liquid drops without tensile instability.