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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Using light and scanning electron microscopy, the morphological adaptations of the yak (Bos grunniens) tongue to its foraging environment in the Qinghai-Tibetan Plateau were studied. The tongue of the yak was compared with that of cattle (Bos taurus). Compared with cattle, yak tongues are on average 4 cm shorter (P < 0.001), and yak consume forages using the labia oris, rather than by extending the tongue into the harsh environment. The lingual prominence of yak is greater (P < 0.001) and more developed than in cattle. The conical papillae on the prominence surface of yak are slightly larger (diameter: P = 0.068 and height: P = 0.761) and more numerous (P < 0.001) than in cattle. The lenticular papillae on the prominence surface of yak are larger (diameter: P = 0.002 and height: P = 0.115) and more numerous (P = 0.007) than in cattle. Such characteristics may improve the digestibility of forage by the grinding of food between the tongue and the upper palate. Filiform, conical, lenticular, fungiform, and vallate papillae were observed on the dorsal surface of the tongues studied; no foliate papillae were observed. The papillae were covered by keratinized epithelium, which was thicker (P < 0.001) in the yak than in cattle. It is suggested that the development of characteristic filiform papillae, and more numerous lingual gland ducts and mucus-secreting pores in the lenticular, fungiform and vallate papillae, fungiform papillae, probably having mechanical functions, are all morphological adaptations by yak to diets with greater fiber and DM content as provided by the plants within the Qinghai-Tibetan Plateau environment. On average, yak has 26 vallate papillae and cattle have 28. In the vallate papillae of the yak, the taste buds are arranged in a monolayer within the epithelium, whereas they are multilayered (2 to 4) in those papillae in cattle. The number of taste buds in each vallate papillae was less (P < 0.001) in the yak than in cattle. Therefore, the gustatory function of the yak was weaker than in cattle. Yaks graze throughout the year on diverse natural grasslands and have evolved morphological characteristics enabling them to consume a wide variety of plant species, thereby better adapting them to the typically harsh characteristics of their pastures.

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

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