## 5 Conclusions  
The productivity of subtropical coniferous forests was examined in this study using the process-based ED-2.2 model to simulate the effects of varied severities of seasonal drought and/or warming. The findings demonstrate that the DBH-height allometric equation's fitting accuracy is superior to that of the default equation in ED-2.2 after model calibration. The ED-2.2 model successfully represented the seasonal variable characteristics of NPP in subtropical coniferous forests. Over time, there was less of a discrepancy between MODIS-NPP and the simulated yearly NPP. Seasonal warming has a smaller impact on NPP than seasonal dryness. The NPP reduction rate changed over time (July-October) in response to seasonal drought and warming situations, respectively. With an average loss of 31.72%, the combined warming-drought scenario in October caused the NPP to decline more dramatically compared with either the drought-only or warming-only scenario alone. Seasonal drought does have a large legacy effect on the productive recovery of subtropical coniferous forests. With the increase in warming severity, the average values of canopy temperature increased, but NPP decreased. Seasonal drought and/or warming limit forest production through decreasing soil moisture and/or increasing canopy air space temperature, which impact on plant photosynthesis and productivity, respectively.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f13122147/s1](https://www.mdpi.com/article/10.3390/f13122147/s1)]([https://www.mdpi.com/article/10.3390/f13122147/s1](https://www.mdpi.com/article/10.3390/f13122147/s1)). Figure S1: ED2 simulated annual NPP vs. MODIS-NPP for separate forest plot; Figure S2: ED2 simulated monthly NPP for separate forest plot under different scenarios.  
**Author Contributions:** Conceptualization, Y.Z.; methodology, M.X., Y.Z. and S.L.; software, M.X. and Y.Z.; validation, M.X. and Y.Z.; formal analysis, M.X.; investigation, M.X. and Y.Z.; visualization, M.X.; resources, D.D.; data curation, M.X.; writing--original draft, M.X.; writing--review and editing, M.X., Y.Z., S.L., D.D., L.Z., M.Z. and Z.W.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z., S.L. and L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (31901241, U20A2089, 41971152, 42107476), the Research Foundation of Education Bureau of Hunan Province, China (21B0277), and the Hunan Innovative Talent Program (2019 RS1062). Aid program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province (S.L.).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We appreciate the data support from the TRY initiative, GMAO, ISRIC, and ORNL DAAC. We appreciate DAAC for providing the MODIS-NPP (MODI7A3 HGF) dataset. We appreciate the ED-2 Model Development Team ([[https://github.com/EDmodel/ED2](https://github.com/EDmodel/ED2)]([https://github.com/EDmodel/ED2](https://github.com/EDmodel/ED2))) (accessed on 16 March 2022) for sharing the source code of ED-2.2.  
**Conflicts of Interest:** The authors declare no conflict of interest.