This paper presents an eco-evolutionary optimality (EEO)-based modeling approach to examine the effects of climate fluctuations and CO₂ levels on GPP during the Last Glacial Maximum (LGM, 21,000 years before present) and the mid-Holocene (MH, 6,000 years before present), relative to pre-industrial conditions (PI). The study effectively integrates climate and vegetation models to assess the influence of CO₂ constraints, climate variability, and solar radiation on plant productivity and C3/C4 competition. However, I have concerns regarding the novelty and broader implications of this work, particularly how it advances beyond previous factorial simulations.

The impacts of changing CO₂ levels on plant growth have already been incorporated into many land surface models (LSMs) and Earth System Models (ESMs), though the magnitude of physiological effects differs between models. The authors should clarify how this study advances prior work and explicitly discuss its implications for ecosystem modeling.

A direct comparison with existing models (e.g., DGVMs, LSMs, and ESMs) would strengthen the study’s contribution. How does the EEO-based approach improve upon these models in simulating GPP and C3/C4 competition?

The study reports an LGM GPP estimate of 84 PgC, within the CMIP6/PMIP4 range of 61–109 PgC. There is large inter-model variability (spanning tens of PgC). The authors conclude that CO₂ effects led to a 3 PgC reduction in GPP during MH, while climate changes contributed to a 2 PgC increase, yielding a net difference of only 1 PgC. Given the large uncertainty in model estimates, is this difference statistically significant? Could this conclusion be influenced by model structural biases or sensitivity to parameter choices?

Zhao et al. investigated the impacts of climate fluctuations and CO2-induced alterations on gross primary production (GPP) using an eco-evolutionary optimality (EEO) based modelling approach. Two contrasting periods are focused, including the Last Glacial Maximum (LGM) and the mid-Holocene (MH), and compared to pre-industrial conditions (PI). This study assessed the importance of CO2, climate change, and light on the GPP at the global scale and pixel level. I have a few concerns about the robustness and implications of this study.

There is a large model range of GPP at the LGM (61-109 PgC yr-1 or 40-110 PgC yr-1), which could be primarily attributed to uncertainties in the effects of tree cover, distribution of C4 plants, and/or effects of climate change and changing CO2. Thus, the uncertainties associated with climate change and CO2 may be much larger than or at least comparable to their effects on the GPP differences between LGM and PI estimated by this study (Figure 5). This substantial uncertainty could raise questions about the robustness and reliability of the results presented in this study.

This study emphasizes the impacts of CO2 on the GPP and vegetation dynamics during both LGM and MH. Different levels of CO2 during LGM, MH, and PI, cause various light-use efficiency and distribution of C4 plants, thereby inducing different GPP. Figure 5 shows the magnitudes of effects of CO2 on the GPP during LGM and MH, thus could be used to estimate the sensitivity of GPP to CO2 changes. Although it is CO2 sensitivity based on the long-term changes, it would be meaningful and interesting to compared it with that based on the recent observations and simulations.

The description of units is confusing. For example, the unit of global GPP is PgC yr-1 rather than PgC. In Table 3, the unit of the contribution of C3/C4 to GPP is gC m2 yr. It should be checked because gC m-2 yr-1 is more commonly used.