This study focuses on the critical environmental and health issue of near-surface ozone pollution in Africa. To address the scarcity of ground-based observational data across the continent, it integrates GEOS-Chem simulations, CMIP6 multi-model data, and a Random Forest (RF) model to construct a research framework with both interpretability and predictability. Innovatively, the study quantifies the independent contributions of climate-driven changes in meteorological conditions and biogenic isoprene emissions to ozone concentrations, thereby illuminating the compound health risks confronting Africa in the context of global warming. The manuscript is detailed, well-structured, and features clear conclusions, making it suitable for publication in the journal Atmospheric Chemistry and Physics. Here are some suggestions for the authors to improve the manuscript.

1. Why did this study choose a hybrid approach combining a random forest model with a chemical transport model (CTM), rather than directly using a multi-model ensemble mean of machine learning models or a single CTM for long-term projections? What specific advantages does this hybrid modeling framework offer for this research?
2. The article mentions that most regions in Africa are in a "NOx-limited" regime. In such an environment, why does an increase in biogenic isoprene emissions lead to a slight decrease in ozone concentrations?
3. The study designed two experiments, O3_MET and O3_ALL, to distinguish the contributions of meteorology and biogenic emissions. In the random forest model, how did the authors ensure that fixing one set of variables does not introduce prediction bias due to autocorrelation among features?
4. Under high-emission scenarios (e.g., SSP5-8.5), which specific changes in meteorological factors (such as radiation, humidity, and cloud cover) enhance the "ozone climate penalty" effect?
5. The results indicate that the impact of rising temperatures on mortality in Africa is far greater than that of ozone concentrations. Is this difference primarily due to variations in the sensitivity of exposure-response functions, or is it determined by the increased frequency of future extreme heat events?
6. The study used multiple CMIP6 model datasets. Why were these particular models selected?
7. What key input data did the GEOS-Chem model primarily provide during the machine learning training phase?
8. The authors adopt a "single-year verification" strategy, designating the period 2000–2009 and 2011–2019 as the training set, with 2010 serving as the sole test year. This approach may fail to fully assess the model’s generalizability across different time periods and climate backgrounds. Ozone concentrations are significantly influenced by interannual meteorological fluctuations, and a single test year cannot cover diverse interannual variability scenarios, potentially underestimating the uncertainty of the model’s projections for future interannual fluctuations. Please supplement the core rationale for selecting 2010 as the exclusive test year. Have more stringent temporal cross-validation methods been implemented (e.g., setting multiple independent test years, time segment splitting such as 2010–2014, or rolling window validation) to evaluate the model’s robustness?
9. SHAP analysis reveals that certain factors contribute most significantly to predicting biogenic isoprene emissions. Why are these factors particularly influential?
10. Which geographical region in Africa is predicted to experience the most significant increase in biogenic isoprene emissions, and why?
11. Apart from temperature and isoprene, what other factors may influence future ozone concentrations under climate change? What are the main sources of uncertainty mentioned in the article, particularly regarding assumptions about land use and population density?
12. To improve the accuracy of regional predictions, what enhancements does the author suggest could be made in machine learning or chemical transport modeling in the future?

The manuscript’s novelty is not yet convincingly established. Several of the stated innovations appear to be incremental combinations of existing data sources and standard analytical tools rather than clearly original methodological contributions. There are multiple global ozone projections under the CMIP6 scenarios employing Climate Chemistry models as well as data-driven methods, such as Li et al., 2026; Ni et al., 2026; Ma et al., 2026; Brown et al., 2022. The current manuscript did not provide enough innovations to construct ozone concentration datasets at either global or regional scale. Meanwhile, it looks like a repetitive work of their own work which applied similar ML techniques in studying global ozone changes under the same CMIP6 sceanrios (Ni et al., 2026)

References:

Brown, F., Folberth, G. A., Sitch, S., Bauer, S., Bauters, M., Boeckx, P., Cheesman, A. W., Deushi, M., Dos Santos Vieira, I., Galy-Lacaux, C., Haywood, J., Keeble, J., Mercado, L. M., O'Connor, F. M., Oshima, N., Tsigaridis, K., and Verbeeck, H.: The ozone–climate penalty over South America and Africa by 2100, Atmos. Chem. Phys., 22, 12331–12352, https://doi.org/10.5194/acp-22-12331-2022, 2022.