This study presents a new framework that couples an Earth System Model with a spatially disaggregated Integrated Assessment Model to examine how climate change (i.e., temperature changes) interacts with the economy. The baseline simulation in the study shows that the economic impacts of global warming differ substantially across regions and that internal climate variability leads to significant volatility in regional GDP, emphasizing the value of high-resolution economic impact assessments. The model that this study presents fills an important gap left by previous frameworks that used coarse spatial aggregation, simplified climate representation, or weak coupling between climate and the economy. However, several aspects should be addressed before the manuscript is ready for publication. 

Major Comments:

1. There are several existing coupled ESM-IAM frameworks (e.g., E3SM–GCAM (Di Vittorio et al., 2025), iESM (Collins et al., 2015; Thornton et al., 2017)), and it would be helpful for the authors to explicitly acknowledge these efforts and situate NorESM2–DIAM within this broader context. Unlike E3SM–GCAM and iESM, which exchange CO₂ emissions, terrestrial productivity, and land-use information, NorESM2–DIAM currently exchanges only CO₂ emissions and temperature, with economic impacts represented through an aggregate productivity function.

While this design allows for fine spatial resolution and transparent temperature–productivity relationships, it omits key land-mediated feedbacks, e.g., those related to land cover, albedo, soil carbon, and evapotranspiration,that are represented in NorESM2’s land module. I understand that as a spatially disaggregated macroeconomic IAM, DIAM occupies a distinct niche relative to process-based IAMs like GCAM, IMAGE, and MESSAGE, which explicitly simulate land, energy, and technological dynamics. A concise discussion of these trade-offs, what NorESM2–DIAM gains in spatial detail and simplicity, and what it sacrifices in process feedbacks, would help readers clearly understand its comparative advantages and intended applications.

references:

Di Vittorio, A. V., Sinha, E., Hao, D., Singh, B., Calvin, K. V., Shippert, T., ... & Bond‐Lamberty, B. (2025). E3SM‐GCAM: A synchronously coupled human component in the E3SM Earth system model enables novel human‐Earth feedback research. Journal of Advances in Modeling Earth Systems, 17(6), e2024MS004806.

Collins, William D., Anthony P. Craig, John E. Truesdale, A. V. Di Vittorio, Andrew D. Jones, Benjamin Bond-Lamberty, Katherine V. Calvin et al. "The integrated Earth system model version 1: formulation and functionality." Geoscientific Model Development 8, no. 7 (2015): 2203-2219.

Thornton, P. E., Calvin, K., Jones, A. D., Di Vittorio, A. V., Bond-Lamberty, B., Chini, L., ... & Hurtt, G. (2017). Biospheric feedback effects in a synchronously coupled model of human and Earth systems. Nature Climate Change, 7(7), 496-500.

2. Explanation of aggregate GDP deviations (Lines 600–602)

The current explanation, that aggregate GDP deviations persist mainly due to spatially correlated temperatures and concentrated economic activity, is not entirely convincing to me. Spatial correlation in temperature does not necessarily translate to homogeneous economic responses. For example, the northern and southern United States likely respond differently to warming: although northern regions show greater temperature increases than southern ones (Figure 11), they still experience gains in GDP per capita, whereas parts of the South show declines (Figure 12). This pattern may reflect differences in each region’s position relative to the optimal temperature shown in Figure 3. I encourage the authors to revise this explanation.

Minor Comments:

1.  Line 586: Please double-check “(see Fig. 4.5)”. I cannot find Figure 4.5. Perhaps this refers to Section 4.5 instead?

2. Figure 13(c): Some regional labels overlap and are difficult to read. I suggest adjusting the layout or font size to improve readability.

3. Figure 5: There appears to be a sharp change between 2100 and 2120. Could the authors clarify the potential reason for this sharp change?