*2.2. Data*

Daily meteorological data from 693 stations in China between 1960 and 2017 were used, including maximum, mean, and minimum temperatures (Tmax, Tmean, and Tmin, respectively); sunshine hours (H); wind velocity (U); relative humidity (Rh); and P. These data were obtained through the meteorological data sharing network of the China Meteorological Administration and were checked for homogenization and quality, including controls for time and space consistency, extreme values, and climate-limit or allowable values [28].

Observations from 44 flux stations were used to validate the ETa model. Nine of these stations are part of China FLUX (The Chinese Terrestrial Ecosystem Flux Research Network). Data from the remaining stations were obtained from published articles. These flux stations are widely distributed in space, including 16 forest, 17 grassland, 6 farmland, and 5 wetland stations, covering the main climatic regions and typical ecosystem types in China. Details on the data are given in Table 1.

**Table 1.** Metadata for each flux station including station name, location, altitude, years of available data, and references.



**Table 1.** *Cont*.

Nos.1 to 16 are forest stations, 17 to 33 are grassland stations, 34 to 39 are cropland stations, and 40 to 44 are wetland stations. Periods in **bold** are the **Validation Data**. Haibei a and Haibei b are two sites in different geographical locations.

Actual evapotranspiration data (unit: mm) and the 0–10 cm depth, monthly average soil water-content data (unit: kg·m<sup>−</sup>2) in GLDAS\_Noah025\_M 2.0 and 2.1 datasets were used, with 0.25 × 0.25◦ spatial resolution and a time range from January 1960 to December 2017. GLDAS data are global land-surface characteristics and flux data generated by advanced land–surface models and data assimilation technology [63].

The validation NPP data are the MOD17A3 surface vegetation NPP data provided by the EOS/MODIS (TERRA satellite) of NASA. MOD17A3 has been verified and widely applied in research regarding vegetation growth, biomass estimation, environmental monitoring, and global change in different regions of China and the world. In this study, we used NPP data with a 0.5 × 0.5 km resolution from 2000 to 2017. Data corresponding to the 693 meteorological stations were extracted using the neighboring grid method.
