Tropospheric ozone is a globally important air pollutant and a short-lived climate forcer, with substantial impacts on human health, climate change, and terrestrial ecosystems. Understanding the relationship between ozone concentration changes and their driving factors is essential for developing effective control strategies. This study utilizes ground-based observational data from the Chinese monitoring network during 2013-2023 and develops a machine-learning-based method to quantitatively disentangle the contributions of meteorological conditions and anthropogenic emissions. The analysis is further extended to evaluate the sensitivity of ozone to climate change. In addition, the authors employ satellite retrievals to explore the changes in precursor ratios and to diagnose the shifts in chemical regimes. The proposed analytical framework provides valuable insights and will be highly informative for future studies. Overall, the manuscript is clearly structured, well-designed, and well-written, and it fits well within the scope of ACP. I would recommend publication after the following issues are addressed:

Title suggestion: Consider revising the title to “Tracking surface ozone responses to clean air actions under a warming climate in China” for clarity and stronger alignment with the scope.

Lines 52-54: This sentence requires additional references. In particular, the 2021 IPCC report should be cited and carefully verified.

Line 81: Provide the full name of XGBoost when first introduced.

Line 89: Please remove the word “monsoon.”

Line 109: The study develops an innovative machine-learning framework for attribution analysis, including an extension to climate change. This is a key contribution, but I suggest adding more technical details, such as a conceptual diagram of the methodology, to improve clarity and accessibility for readers.

Line 114: The role of time variables requires clarification. Were the diurnal and seasonal/monthly variables included to remove short-term and seasonal variability, leaving the long-term trend for quantitative attribution? Please explain explicitly.

Line 125: Why was the modeling performed separately for each city, rather than by grouping cities into regions? Please explain the rationale.

Lines 152-161: The uncertainty analysis, particularly for the Fixed Emission Approximation (FEA) method, is highly valuable. I strongly recommend moving these results (currently Figure S3) into the main text.

Lines 202-205: The manuscript highlights several regions in China. Please explain why these regions were emphasized and include a map showing their geographic distribution for better context.

Line 227: The references here primarily address ecological impacts, yet the text mentions “both human and ecological health.” Please provide more references specific to human health. Also, revise “ecological health” to “ecosystem health.”

Line 229: The phrase “reflecting initial policy effectiveness” is unclear. Please rephrase for precision.

Lines 230-232: The conclusion drawn here seems overstated, as the evidence provided is insufficient. This section mainly discusses temporal and spatial ozone concentration trends. A more cautious interpretation is recommended: instead of attributing trends directly to policy effectiveness, the authors could note that observed trends occurred under varying emission control backgrounds, while meteorology also played an important role.

Line 243: Please define the parameters τ and p shown in Figure 1.

Line 276: Correct “Emission-driven” to “emission-driven.”

Lines 279-281: The logic here is confusing. The discussion first emphasizes the role of anthropogenic emissions, but then suggests that changes in emissions highlight the role of meteorology. Please clarify or restructure this argument.

Lines 299-300: Please rephrase the sentence. The term “near-baseline” is ambiguous and requires clarification.

Line 342: There is an editorial error that needs correction.

Line 390: I recommend revising the y-axis labels for greater accuracy. For instance, in panel (a), the label currently suggests “extreme weather,” but it actually represents only “extreme heatwave”. In contrast, panel (b) provides a more specific description. The labeling should be made consistent and precise to avoid potential misinterpretation.

Line 397: The title of this section should be revised, since the authors are not reconstructing the ozone trend per se. A more accurate option could be “Reshaping distributions of ozone controlled by a warming climate.” This section is indeed interesting and methodologically innovative. However, the manuscript should elaborate more clearly on which specific factors are included in the climate-change-driven trend, especially considering the constraints posed by the limited length and coverage of historical observational records.

Line 331 (Figure 3): Ensure the map format is consistent with that in the Supplementary Figures.

Lines 374-376: Since the SHAP interpreter is a key tool used to analyze predictor contributions, it should be briefly described in the Methods section.

Line 437: In Figure 5, the authors present both climate-change-driven and emission-driven trends. I am curious about how the results from the proposed FEA method compare with those from other widely used machine-learning approaches for trend analysis, such as de-weather. A comparison between different methods would not only be interesting but also serve as a useful validation of the robustness of the proposed framework.

Line 445 (Conclusion): The conclusion is overly lengthy. Please condense and refine this section for clarity and impact.

General Comments:

The manuscript provides valuable insights into surface ozone trends in China, driven by emissions and meteorological factors, using the Fixed Emission Approximation (FEA) method. While the study is important, several key issues require attention before it can be accepted.

1. The explanation of emission surrogates and the impact of coarse-resolution meteorological data on the regression process is insufficient. Additionally, regional ozone trends, especially in the PRD, need clearer explanations, and the manuscript should include maps or time-series for key ozone precursors (CO, NOx, VOCs).
2. The discussion on the COVID-19 lockdown requires more detailed analysis of TROPOMI data to explain regional ozone changes, and maps for regions like the North China Plain would help clarify spatial patterns. Figures, particularly Figure 3, lack clear quantitative results, and Section 3.3 requires deeper analysis.
3. Finally, the roles of temperature, humidity, and radiation in ozone formation need clearer interpretation.

In conclusion, a major revision is needed to address these issues, strengthen the analysis, and improve the clarity and presentation of the results.

Specific Comments:

Line 193: What is TAP?

Line 114: How does the author account for the uncertainty in coarse-resolution meteorological variables and site data during the RF regression process? Is the specified elevation referring to the site elevation or the average elevation of the coarse-resolution meteorological variable grid?

Line 115: What is an emission surrogate? Where did the author obtain it? It should be mentioned in Section 2.1.

Line 117: “The aforementioned variables”: It’s not clear.

Line 127-129: Rewrite this sentence.

Line 243: “Anthropogenic drivers”  “Anthropogenic emission drivers”.

Line 243: You should show the anthropogenic emission map or time-series line for major ozone precursor such as CO, NOx, and VOC.

Line 249-251: The author needs to explain why the PRD is rising and why it is falling in other regions.

Line 260: Authors should show a map to tell reader where is northern China?

Line 260-262: Prior to 2017, PRD consistently recorded the lowest ozone concentrations among these major regions. Intuitively, the PRD appears to have the least potential for ozone reduction. Yet why did both the YRD and SCB regions exhibit smaller reduction margins than the PRD?

Line 266-268: The author should inform readers: How much have anthropogenic emissions decreased in major regions due to the nationwide lockdown?

Line 273-275: The author suggests that the rise in ozone concentrations is due to suppressed NO titration. Therefore, the author could fully utilize TROPOMI data to analyze changes in sensitive zones during the COVID-19 period. This may help explain why ozone concentrations in the remaining 20% of cities decreased during the COVID-19 pandemic.

Line 275-276: The author should explain how anthropogenic emissions have changed during this period.

Line 295: Where is North China Plain?

Line 297: “The Yangtze River Delta”  “YRD”.

Line 298-299: You can show a ∆temperature map from ERA5 data.

Line 306-307: The author might use this to explain why the PRD's changes differ from those of other cities in Section 3.2.

Figure 3: It is recommended that the author label the locations of major regions on this map.

Section 3.3: Lack of quantitative results. Figure 3 fails to clearly show the interannual changes in sensitive areas. The authors should adopt a more explicit presentation method to highlight the differences in area among sensitive zones in each region.

Line 337-350: Authors cannot merely provide quantitative descriptions; ACP journals require authors to conduct deeper analysis of these quantitative results.

Line 356-357: That’s a boring sentence. Although the author provides some explanation in lines 358-359, this explanation is overly broad. For example, why are RH and temperature the dominant factors in other regions, while shortwave radiation and RH are the dominant factors in the YRD region? What is the relationship between temperature and radiation? Which variable is the fundamental cause of ozone concentration changes, and what role does RH play in this process?

Line 369-371: Figure S13 shows that the correlation between T and SR in inland southern China is very low during both HW and NHW periods. Why is this? Additionally, the correlation for RH between HW and NHW appears significantly different. I recommend that the authors adjust the colorbar in Figure S13E-F to a red-blue scale with zero centered. This may reveal positive correlations for RH in certain regions.

Line 379: “amplifying”  “increasing”.

Line 383-384: I do not agree that. Cloud cover directly influences temperature and shortwave radiation variations and should not be considered a secondary effect.

Line 387: What is the RH effect?