## 2 Materials and Methods
### Model Configuration and Simulation Setup
#### 2.2.5 Simulation Scenarios of Disturbance Types and Severities  
In the ED-2.2 model, we performed an interannual calculation of NPP for each forest plot to ensure that each plot had been affected by drought and/or warmings of varying severities in July, August, and September. Drought scenarios were based on the Grades of Meteorological Drought [56]; and warming scenarios were based on the projections of possible temperature increases by the IPCC in 2021 and 2050 [27] and some existing studies [11; 78; 79].  
Scenario settings. We set up 10 scenarios of four types (Table 4). Type 1 was baseline (B0). Type 2 was drought-only scenarios, including mild drought (D1, P\({}_{0}\)\(\times\) 0.6), moderate drought (D2, P\({}_{0}\)\(\times\) 0.4), and severe drought (D3, P\({}_{0}\)\(\times\) 0.2). Type 3 was a warming-only  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Functional Type** & **Allometric Equation** & **RMSE** & **MAE** \\ \hline \multirow{3}{*}{PFT2} & Height = 61.7\(\cdot\)(\(1-\exp(-0.035\cdot\text{DBH}^{0.695})\)) A & 2.58 & 6.66 \\  & Height = 61.7\(\cdot\)(\(1-\exp(-0.026\cdot\text{DBH}^{0.715})\)) B & 1.68 & 2.84 \\  & \(-\)34.7\% & \(-\)57.4\% \\ \hline \multirow{3}{*}{PFT4} & Height = 61.7\(\cdot\)(\(1-\exp(-0.042\cdot\text{DBH}^{0.522})\)) A & 2.58 & 6.67 \\  & Height = 61.7\(\cdot\)(\(1-\exp(-0.026\cdot\text{DBH}^{0.717})\)) B & 1.66 & 2.75 \\ \cline{1-1}  & \(-\)35.8\% & \(-\)58.8\% \\ \hline \hline \end{tabular}  
* A and B indicate the default and calibrated DBH-height allometric equations, respectively. The percentage represents the rates of change in RMSE and MAE between before and after calibration.  
\end{table}
Table 3: Calibration of DBH-height allometric equation for PFT2 and PFT4.  
scenario, including mild warming (W1, T\({}_{0}\) + 1.0), moderate warming (W2, T\({}_{0}\) + 1.5), and severe warming (W3, T\({}_{0}\) + 2.0). Type 4 was drought-warming scenarios, including mild (D1 + W1, P\({}_{0}\) \(\times\) 0.6, and T\({}_{0}\) + 1.0), moderate (D2 + W2, P\({}_{0}\) \(\times\) 0.4, and T\({}_{0}\) + 1.5), and severe (D3 + W3, P\({}_{0}\) \(\times\) 0.2, and T\({}_{0}\) + 2.0) (Table 3). We assumed the future drought and/or warming occurred after 5 years (from 2021, i.e., 2026), and we also assumed each climate scenario occurred consecutively in July, August, and September [80; 81] in 2026.  
Figure 1: Monthly ED-2.2 simulated NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)] for averages of 10 forest inventory plots from 2005 to 2021 in the study. Boxplots were based on 10 plots simulation results for 12 months. Black points indicate outliers.  
Figure 2: Annual ED-2.2 simulated NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)] vs. MODIS-NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)]. The green line is the annual model simulated NPP for an average of 10 plots, shaded as its confidence interval; the yellow line is the annual MODIS-NPP for an average of 10 plots, and the shading is mean relative error (23.58%).  
In order to compare the difference in coniferous forest NPP between baseline and disturbance scenarios, we used the boxplot which is based on the simulation results of ten plots (including _P. massoniana_ forests and _C. Ianccolata_ forests) over the course of a year.