## 3 Results and Discussion
### Annual Scale
#### 3.1.2 Gross Primary Productivity (GPP)  
As shown in Table 3 and Figure 2b, the model generally overestimates annual mean GPP. The overall simulated annual mean GPP is 1346.85 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\), compared to the observed mean of 1295.48 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\), resulting in an absolute error of 51.37 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\) and a relative error of 3.97%. The overall correlation coefficient (R) is 0.67, indicating a moderate positive relationship, but the RMSE of 500.62 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\) suggests substantial  
Figure 2: Comparison of modeled versus observed annual mean for **(a)** evapotranspiration (ET) and **(b)** gross primary productivity (GPP) fluxes across 12 vegetation types. Dashed lines indicate a 1:1 relationship, and solid lines show linear regression fits, with correlation coefficients (R) displayed.  
scatter. Except for ENF, SAV, and GRA, simulated values are higher than observations for most other vegetation types. The largest overestimation occurs for OSH, with a relative error of 53.99%.  
Among vegetation types, CSH shows the best correlation (R = 0.97, \(p\) < 0.001), indicating strong agreement, although the bias is relatively large (absolute error 184.99 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\), relative error 20.06%). OSH performs the worst with a negative correlation (R = \(-\)0.72, \(p\) = 0.2793, not significant) and significant overestimation (observed 472.18 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\) vs. simulated 727.10 gC\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\)), possibly due to inadequate simulation of vegetation transpiration processes in arid regions. The lack of significance for OSH and DNF (\(p\) = 0.9428) might again be due to small sample sizes. For ENF, EBF, CSH, WSA, SAV, GRA, WET, and CRO, the \(p\)-values are <0.001, indicating statistically significant results.  
The overestimation of annual GPP may originate from the simplified parameterization in Noah-MP's photosynthesis module, particularly its treatment of photosynthetic capacity metrics. While the model employs the [PERSON] biochemical framework ([PERSON], 1995), its reliance on static lookup tables for key parameters, such as the maximum carboxylation rate (V\({}_{\text{cmax}}\)) and the maximum electron transport rate (\(\eta_{\text{max}}\)), introduces critical limitations. However, plant photosynthetic capacity exhibits considerable variability across species, within species, and even among different leaves of the same individual; it also undergoes dynamic adjustments in response to environmental conditions (e.g., light availability, temperature, moisture, CO\({}_{2}\) concentration, nutrient status) and leaf age ([PERSON], 1995). Consequently, if the model's preset average values for V\({}_{\text{cmax}}\) or J\({}_{\text{max}}\) for a given PFT are systematically higher than the actual photosynthetic capacity of that PFT in specific regions or during particular years, this directly leads to an overestimation of the potential photosynthetic rate. This, in turn, can accumulate, resulting in an overestimation of annual GPP.