## 2 Materials and Methods
### Data
#### 2.1.1 Meteorological Data  
Average daily \(T_{\mathrm{a}}\), \(T_{\mathrm{amin}}\), \(T_{\mathrm{amax}}\), average relative humidity (RH), and sunshine duration for 1981-2012, available from 754 conventional meteorological stations, were used. The average RH was converted to VPD following the reference methodology of [PERSON] et al. (2018). Of the 754 meteorological stations, \(R_{\mathrm{g}}\) and \(R_{\mathrm{d}}\) were measured at 103 and 81 sites, respectively, and sunshine duration was measured at all 754 sites during 1981-2012 (Figure 1 and Supplementary Figure S1, and Supplementary Tables S1 and S2). The \(R_{\mathrm{g}}\) at the site scale was interpolated to the national scale by the following steps. First, the Angstrom model (Equation (1)) [29] was calibrated using data from 103 meteorological stations recording both \(R_{\mathrm{g}}\) and sunshine duration measurements during 1981-2012. Second, to compensate for the lack of global radiation measurements, \(R_{\mathrm{g}}\) for the remaining 651 meteorological stations was calculated by using sunshine duration and the calibrated Angstrom model in step 1. Third, data from 12 sites with \(R_{\mathrm{g}}\) measurements in 2014 were used to validate the calibrated Angstrom model (see  
Figure 1: Distribution in China of meteorological stations recording sunshine duration, and global and diffuse radiation. Supplementary Materials Figure S2). Finally, using radiation data at the 754 sites, \(R_{\rm g}\) was interpolated to the national scale with an inverse distance weighted method. Considering the heterogeneity of terrain and climate in China, the country was divided into eight regions: Northeast China (R1), Inner Mongolia (R2), Northwest China (R3), North China (R4), Central China (R5), South China (R6), Southwest China (R7), and the Qinghai-Tibet Plateau (R8). The \(R_{\rm g}\) estimation model was fitted for each region separately to convert sunshine duration into \(R_{\rm g}\), which was comparable with the method of [PERSON] et al. (2019); [PERSON] et al. (2020). The Angstrom model ([PERSON] et al., 2019) was selected to estimate \(R_{\rm g}\) because of the advantages of a simple structure, clear physical meaning, stable model coefficients, and good fitting results in addition to its widespread use ([PERSON] et al., 2019). This model was as follows:  
\[\frac{R_{\rm g}}{R_{0}}=a+b\times\frac{n}{N} \tag{1}\]  
where \(R_{\rm g}\) is the global radiation reaching the surface, \(R_{0}\) is the daily extraterrestrial radiation, \(n\) is the actual daily sunshine duration observed at a given site, \(N\) is the potential daily sunshine duration, and \(a\) and \(b\) are coefficients. Both \(R_{0}\) and \(N\) can be calculated based on latitude and date ([PERSON] et al., 2019). Root mean square error (RMSE) values of the radiation derived using Angstrom models for each of the eight regions of China are shown in Table 1.  
The \(R_{\rm d}\) at the site scale was interpolated to the national scale based on the following steps. First, data from 81 meteorological stations recording both \(R_{\rm g}\) and \(R_{\rm d}\) during 1981-2012 were used to calibrate the logistic model (Equation (2)) ([PERSON] et al., 2019; [PERSON] et al., 2020). Second, to compensate for the lack of \(R_{\rm d}\) measurements, \(R_{\rm g}\) estimated with the Angstrom model was used to calculate \(R_{\rm d}\) for the remaining 673 meteorological stations using the calibrated Equation (2) in step 1. Third, data from 12 sites with \(R_{\rm g}\) measurements in 2014 were used to validate the calibrated Angstrom model (see Supplementary Materials, Figure S3). Finally, \(R_{\rm d}\) was interpolated to the national scale using \(R_{\rm d}\) at the 754 sites. A simple and accurate logistic model ([PERSON] et al., 2019; [PERSON] et al., 2020) was used to estimate \(R_{\rm d}\) as follows:  
\[\frac{R_{\rm d}}{R_{\rm g}}=\frac{1}{1+e^{c+d\times\frac{R_{\rm g}}{R_{0}}}} \tag{2}\]  
where \(R_{\rm d}\) is the diffuse radiation, and \(c\) and \(d\) are the nonlinear regression coefficients.