## 3 Results
### Relationship between GPP\({}_{\text{GS}}\) and Meteorological Factors  
Figure 4 shows a warming, wetting, and dimming trend across the whole NEC, and the impacts of this climate change on vegetation GPP\({}_{\text{GS}}\) are analyzed in the following sections. Table 1 indicates that GPP\({}_{\text{GS}}\) of the NEC is related with the three meteorological factors (daily average temperature, precipitation, and daily solar radiation), especially the obviously warming; nevertheless, this correlation has a great spatial heterogeneity. In plains, warming (Figure 4a) can improve the productivity of crops, while precipitation (Figure 4b) and solar radiation (Figure 4c) do not have significant impacts. In mountainous areas, positive impacts of warming on forest productivity only exist in high-latitude GKM and LKM, and solar radiation comes into positive effect on GPP\({}_{\text{GS}}\) in GKM and CBM. Precipitation has a negative effect on GPP\({}_{\text{GS}}\) of forests according to the correlation results, nevertheless, those negative effects were not significant when eliminating the coupling effects between temperature and radiation with precipitation as partial correlation coefficients display. The difference in correlation and partial correlation results manifest that in the NEC, precipitation indirectly impacts vegetation growth by affecting temperature and solar radiation. For example, there is a remarkable dip in GPP\({}_{\text{GS}}\) time series of the GKM and LKM in 2003 (Figure 3b,c), which is in line with the small amount of radiation caused by extreme large quantities of precipitation in 2003. The contribution of each meteorological factors on GPP\({}_{\text{GS}}\) can be quantified using the SMIR method, and thus the dominant meteorological factors for GPP\({}_{\text{GS}}\) variation over the last three decades can be identified as shown in Figure 5. In mountainous areas, the combination of the three meteorological factors has great impacts on forest productivity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Precipitation**} & \multicolumn{2}{c}{**Solar Radiation**} \\ \cline{2-7}  & **CC** & **PCC** & **CC** & **PCC** & **CC** & **PCC** \\ \hline
**Plain** & 0.467 \({}^{**}\) & 0.479 \({}^{**}\) & 0.018 & \(-\)0.149 & \(-\)0.134 & \(-\)0.218 \\
**GKM** & 0.501 \({}^{**}\) & 0.512 \({}^{**}\) & \(-\)0.433 \({}^{**}\) & \(-\)0.165 & 0.776 \({}^{**}\) & 0.695 \({}^{**}\) \\
**LKM** & 0.590 \({}^{**}\) & 0.628 \({}^{**}\) & \(-\)0.376 \({}^{*}\) & \(-\)0.202 & 0.389 \({}^{*}\) & 0.189 \\
**CBM** & 0.238 & 0.177 & \(-\)0.483 \({}^{**}\) & \(-\)0.199 & 0.567 \({}^{**}\) & 0.391 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{\text{a}}\) Significant at the 5\(\%\) level; \({}^{**}\) significant at the \(\%\) level.  
\end{table} 
Table 1: Correlation coefficient (CC) and Partial correlation coefficient (PCC) between regional accumulated GPP (GPP\({}_{\text{GS}}\)) and meteorological factors (daily average temperature, precipitation and daily solar radiation) in different regions (Plain, GKM refers to the Greater Khingan Mountain, LKM refers to the Lesser Khingan Mountain, and the CBM refers to the ChangBai Mountain). Figure 4: The inter-annual trend of (**a**) yearly average temperature, (**b**) yearly accumulated precipitation, and (**c**) yearly average SRAD (Shortwave Radiation) within the growing season across the NEC from 1982 to 2015. The inset map in Figure 5 shows that 70.5% of the GPPGS variation in LKM is caused by these three factors among which temperature is the dominant factor. Meteorological factors can explain 61.0% of the variation in GPPGS of CBM where solar radiation is the dominant factor. In GKM, climate effects accounts for 84.0% of the variation in GPPGS, and both temperature and solar radiation are equally important for vegetation productivity.