## 2 Materials and Methods
### Data Sources  
In this study, the global OCO-2-based solar-induced chlorophyll fluorescence product (GOSIF) GPP and the Global Land Evaporation Amsterdam Model version 3.5a (CLEAM) ET remote sensing products were used to calculate WUE. GOSIF GPP (2001-2020) is a long-term series dataset based on solar-induced chlorophyll fluorescence ([[http://data.globalecology.unh.edu/](http://data.globalecology.unh.edu/)]([http://data.globalecology.unh.edu/](http://data.globalecology.unh.edu/)), accessed on 15 May 2022), with a spatial resolution of 0.05\({}^{\circ}\)and 8-day time step [34]. CLEAM product ET is estimated from observed precipitation, surface soil moisture, and vegetation moisture content as the control conditions by using Priestley-Taylor algorithms and combining multi-source remote sensing data [35; 36]. The CLEAM 3.5a datasets ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 15 May 2022) span from 1981 to 2020, with a spatial of 0.25\({}^{\circ}\)and a daily time step. ET products were resampled to 0.05\({}^{\circ}\) to unify the spatial resolution of the ET and GPP products.  
Meteorological datasets include daily temperature, precipitation, and sunshine hours data from the China Meteorological Data Network ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 15 May 2022). This network covers meteorological data from 118 stations in Inner Mongolia from 2001 to 2020, as shown in Figure 1a. Using the FAO Penman-Monteith formula, the sunshine hours were used to calculate solar radiation data [37]. These data were interpolated to a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05 using the kriging interpolation method. Vegetation type data were derived from a 1:1,000,000 raster map of vegetation type of Inner Mongolia with a spatial resolution of 0.0083\({}^{\circ}\). In this map, the study area is divided into 9 vegetation types. In this study, the Gobi Desert was excluded because its vegetation was relatively sparse and some areas even had no vegetation cover.  
Elevation Data (DEM) with a resolution of 90 m and land use/land cover data (2000, 2010, and 2020) with a resolution of 1 km were drawn from the Resource and Environment  
Figure 1: Overview of the study region, **(a)** Geographical location and meteorological stations, **(b)** Vegetation types, and **(c)** Digital elevation model (DEM).  
tal Science Data Platform of the Chinese Academy of Sciences ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 15 May 2022). The land use data mainly included 6 primary land use types (Cropland, Forest, Grassland, Water bodies, Built-up land, and Unused land), and 25 secondary land use types. Considering the characteristics of the wide distribution of grasslands in Inner Mongolia, the land cover types were reclassified into 9 categories, namely cropland, forest, high-coverage grassland, medium-coverage grassland, low-coverage grassland, water bodies, built-up land, sandy land, and unused land. The degree of land use dynamics was also calculated for each league or city during the study period. A single dynamic degree reflecting the change of a specific land use type was assessed in the study area through the study period. The specific calculation formula is available in [PERSON] et al. [38].