## 2 Materials and Methods
### Data  
Daily precipitation, relative humidity, maximum temperature (T\({}_{\rm max}\)), and minimum temperature (T\({}_{\rm min}\)) data spanning the period of 2001-2020 were obtained from the Climate Change Research Center of the Chinese Academy of Sciences ([[https://ccrc.iap.ac.cn/](https://ccrc.iap.ac.cn/)]([https://ccrc.iap.ac.cn/](https://ccrc.iap.ac.cn/)), accessed on 1 September 2023). This dataset has a 0.25\({}^{\circ}\) spatial resolution and incorporates observations from around 2400 meteorological stations across China. It was subjected to rigorous quality checks prior to its release. To gather daily SM values, we utilized the Chinese version 1.0 of in situ data, SMCII.0, accessed through the National Tibetan Plateau/Third Pole Environment Data Central, China ([[https://data.tpdc.ac.cn/zh-hans/data/49b22](https://data.tpdc.ac.cn/zh-hans/data/49b22) de9-5d85-44f2-a7d5-a1 ccd17086d2]([https://data.tpdc.ac.cn/zh-hans/data/49b22](https://data.tpdc.ac.cn/zh-hans/data/49b22) de9-5d85-44f2-a7d5-a1 ccd17086d2), accessed on 1 September 2023). We focused on SMCII.0 data for the period 2001-2020, which has a spatial granularity of 0.1\({}^{\circ}\) (approximately 9 km), encompassing 10 layers spaced at 10 cm intervals. For ease of understanding and analysis, SM at 10 cm is referred to as the 0-10 cm average SM in this study, the SM at 20 cm is referred to as the 10-20 cm average SM, and so on, and the SM at 100 cm is referred to as the 90-100 cm average SM. The dataset was enriched by incorporating various inputs, including ERA5  
Figure 1: (**a**) Location of TRHR, (**b**) 2001 land cover, (**c**) 2020 land cover. Land meteorological forcing data, leaf area index, land cover types, topography, and soil properties, using a robust machine learning approach (random forest) [31]. The in situ SM observations from 1648 stations were employed as the SM modeling target after quality control procedures. After the SMCI1.0 dataset was validated by in situ SM observations, it became a useful complement to existing model-based and satellite-based SM datasets for various hydrological, meteorological, and ecological analyses and modeling processes. The 0-100 cm SM was used as the root-zone SM, which was calculated by combining the weights of each soil thickness. SIF is an intrinsic tool for studying the relationship between vegetation photosynthesis and environmental dynamics [8]. This pivotal role positions SIF as a suitable approximation of GPP in terrestrial ecosystems. In this study, three types of SIF datasets (2001-2020) were used to investigate vegetation growth under compound drought conditions in the TRHR: the contiguous SIF (CSIF), the global orbiting carbon observatory-2 (OCO-2) SIF (GOSIF), and the reconstructing tropospheric monitoring instrument (TROPOMI) SIF (RTSIF). These three SIF datasets were subjected to rigorous quality control prior to release. The CSIF dataset was generated based on moderate-resolution imaging spectroradiometer (MODIS) surface reflectance and OCO-2 SIF data using a trained neural network approach [32]. The CSIF has a 0.05\({}^{\circ}\) spatial resolution and four-day temporal resolution under clear-sky conditions. The CSIF was obtained from the National Tibetan Plateau/Third Pole Environment Data Central, China, and is available online at [[https://data.tpdc.ac.cn/zh-hans/data/d7](https://data.tpdc.ac.cn/zh-hans/data/d7) cccf31-9 bb5-4356-88a7-38c5458f052b]([https://data.tpdc.ac.cn/zh-hans/data/d7](https://data.tpdc.ac.cn/zh-hans/data/d7) cccf31-9 bb5-4356-88a7-38c5458f052b), accessed on 1 September 2023. The GOSIF dataset was constructed with discrete OCO-2 SIF soundings, MODIS remote sensing data, and a meteorological reanalysis [33]. Monthly GOSIF data with a 0.05\({}^{\circ}\) spatial resolution were utilized in this study.