Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods. Validation shows that TPHiPr is generally unbiased and has a root mean square error of 5.0 mm d−1, a correlation of 0.76 and a critical success index of 0.61 with respect to 197 independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art satellite-based dataset (IMERG) and the multi-source merging datasets (MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect precipitation extremes compared with these widely used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).

Afforestation is regarded as the most appropriate approach to control soil erosion (Jia et al., 2017;Porto et al., 2009), but expanding vegetation needs more water to maintain its growth (Huang et al., 2020;Teuling et al., 2019;Q. Wang et al., 2021). Precipitation thus plays a crucial role in the sustainability of afforestation, especially for water-limited areas. Atmospheric moisture is of great relevance to precipitation generation (Payne et al., 2020). There are two sources for atmospheric moisture overlying a land area: the first is external moisture flowing into this area by horizontal advection and the second is internal moisture provided by the local evapotranspiration (ET) (Y. Zhao & Zhou, 2021). The ratio of precipitation transformed from internal recycled moisture to the total precipitation received by this area is defined as the precipitation recycling ratio (PRR) (Holgate et al., 2020), which can serve as an indicator of the intensity of the water cycle (Gao et al., 2020;Trenberth, 1999). Both anthropogenic activities and climate variability affect the transport of atmospheric moisture and its transformation to precipitation (Baudena et al., 2021;Tuinenburg & Staal, 2020). To design science-informed and sustainable policies for afforestation, there is a growing need to explore the response of the atmospheric water cycle to afforestation and climate change.Afforestation alters moisture and energy fluxes interchanged between the atmosphere and land surface by affecting surface characteristics (Baudena et al., 2021;Hagos et al., 2014;. The Loess Plateau (LP) in China is an ideal platform to study the impact of large-scale afforestation on the atmospheric water cycle, since it launched the world's largest afforestation program (Cao et al., 2019), the Grain for Green Program (GFGP) in 1999. With an investment of US$54.57 billion (Bryan et al., 2018), the GFGP has led to a 57% decline

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This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.