## Result
### Attributions of Long-Term Changes in GPP  
Figure 5 displays the spatial pattern of primary impacting variables and Figure 6 shows the Pearson correlation coefficient between the individual factor and GPP annual variation. At a national scale, the correlation coefficients (R) were 0.318 (LAI), 0.225 (air temperature), 0.16 (CO\({}_{2}\)), 0.053 (precipitation), \(-\)0.012 (SRAD) and \(-\)0.056 (AOD), respectively. At the basin scale, different factors had obvious regional discrepancies in the impacts of the annual variation of GPP [9; 59]; these regional discrepancies were mainly reflected in the differences in hydrothermal conditions and the strength of human activities. The average value and standard deviation of the Pearson correlation coefficient in each basin are summarized in Table 2.  
The hydrothermal conditions of water and air temperature and their interaction are generally considered the main climate factors affecting vegetation growth and distribution [60]. In our study, GPP and air temperature did show a relatively positive correlation in most parts of China, except the PRB and the SEB, which can be related to the fact that high air temperature in summer is beyond the optimal growth range of plants, leading to an increase in evapotranspiration and a decrease in soil water availability for vegetation, and thus inhibiting the growth of plants [57; 59]. Notably, the positive correlation between GPP and air temperature in the SRB was not as substantial as in the other basins, probably owing to the reduction in precipitation that weakens the positive effect of warming on GPP [61]. A discrepancy in the GPP-precipitation correlation between the north and \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multicolumn{2}{c}{**Air Temperature**} & \multicolumn{2}{c}{**SRAD**} & \multicolumn{2}{c}{**Precipitation**} & \multicolumn{2}{c}{**LAI**} & \multicolumn{2}{c}{**AOD**} & \multicolumn{2}{c}{**CO\({}_{2}\)**} \\ \cline{2-13}  & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** & **Mean** & **STD** \\ \hline SRB & 0.145 & 0.212 & 0.065 & 0.305 & \(-\)0.03 & 0.343 & 0.4 & 0.217 & \(-\)0.126 & 0.254 & 0.229 & 0.479 \\ CB & 0.265 & 0.266 & \(-\)0.327 & 0.208 & 0.263 & 0.222 & 0.307 & 0.249 & \(-\)0.132 & 0.198 & 0.013 & 0.484 \\ HRB & 0.206 & 0.155 & \(-\)0.241 & 0.135 & 0.372 & 0.172 & 0.501 & 0.198 & 0.101 & 0.186 & 0.204 & 0.385 \\ YERB & 0.252 & 0.165 & \(-\)0.201 & 0.208 & 0.224 & 0.189 & 0.427 & 0.239 & 0.008 & 0.211 & 0.268 & 0.431 \\ HURB & 0.225 & 0.164 & \(-\)0.029 & 0.218 & 0.143 & 0.215 & 0.364 & 0.187 & 0.052 & 0.216 & 0.251 & 0.34 \\ YRB & 0.291 & 0.214 & 0.178 & 0.269 & \(-\)0.03 & 0.228 & 0.271 & 0.225 & 0.021 & 0.243 & 0.21 & 0.483 \\ SWB & 0.359 & 0.194 & \(-\)0.003 & 0.24 & \(-\)0.038 & 0.22 & 0.228 & 0.214 & \(-\)0.204 & 0.155 & \(-\)0.101 & 0.438 \\ SEB & \(-\)0.002 & 0.196 & 0.231 & 0.168 & \(-\)0.194 & 0.159 & 0.057 & 0.195 & \(-\)0.072 & 0.212 & 0.174 & 0.359 \\ PRB & 0.039 & 0.208 & 0.134 & 0.146 & \(-\)0.186 & 0.16 & 0.152 & 0.272 & \(-\)0.025 & 0.311 & 0.176 & 0.432 \\ China & 0.225 & 0.229 & \(-\)0.012 & 0.307 & 0.053 & 0.29 & 0.318 & 0.248 & \(-\)0.056 & 0.246 & 0.16 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Area-averaged (mean) correlation coefficient and its standard deviation (STD) in each basin. Figure 4: Annual and seasonal GPP trends from 1982 to 2015 in each basin. The error bars reflect the standard deviation of the trends. south regions was observed (Figure 6c), i.e., the correlation decreased from north to south. Areas with positive correlation were mainly distributed in the CB, the HRB and the YERB, which possess relatively little precipitation. This implies that, in arid areas, the increase in precipitation had a positive effect on GPP, while the sensitivity of GPP to precipitation was less prominent in humid areas.