This paper analyzes the cause of 2015-2020 positive ozone trends in megacities in China. While the topic is important, the main conclusion of this study is not well supported by the analyses. In addition, the presentation, including the logic, word expression, and figure quality requires substantial improvement. I recommend at least a major revision before it can be re-considered to be published in ACP. 

Main concern:

The authors emphasize the role of meteorology and diminish that of emission change as the key cause of the ozone rise between 2015-2020 in many places of the text, but in most places, there is no direct evidence to approve or disapprove, and the conclusions seem to be arbitrary, I list several questions related to this point below that need to be addressed.

(1)    Line 166-167: Why is this simply attributed to weather system? (e.g. lines 166-168, 178-180, and many others).

(2)    Line 177-180: For what reason it is “highly unlikely emission change”? It is not convincing to guess the cause simply based on the spatial pattern of ozone change. In addition, the ozone data used in this study is smoothed from observations in the TAP dataset, thus it may not reflect the true ozone change in the regions with no direct observation.

(3)    The function of Section 3.2 is very confusing to me. The paper is about 2015-2020 trends, but here only the difference between 2015 and 2017 is analyzed. Even though the ozone difference between 2017 and 2015 is mostly driven by weather anomalies, the authors do not explain what weather system can sustain high ozone from 2017 to 2018-2020. Analyses of yearly differences cannot be simply applied to explain the 6-year trend.

(4)    The authors list “ozone at high ozone stations unchanged while ozone at low ozone station increase” as a major finding (Line 235). If it is driven by weather, I wonder what weather system could selectively influence sites with different ozone levels. It more likely reflects chemical factors. This is a key concern.

(5)    Line 210-223: VOCs emission change is not considered here. And again this is for difference between 2015 and 2017, it doesn’t explain the trend at all.

(6)    Section 3.3 is not convincing as well. It should try to explain what weather system contributes to more ozone consecutive day from 2015 (as authors state that it drives the ozone increase), but figures are not helpful for this purpose. Figure 11 only shows that meteorological parameters can explain some of the ozone variability, Figures 12-13 show that WPSH can influence weather patterns, but is there any hint of an increasing influence of WPSH on consecutive ozone days? It might be useful to first clarify the weather patterns for consecutive ozone days, and explain what system can explain an increase in the frequency of such weather pattern during 2015-2020.

Other comments

Line 100, Before Table 1, please consider introducing the purpose for such classification.

Line 116: missing ppb

Line 158-160. It is quite unclear what the calculation stands for, and how it leads to the conclusion that. Please clarify. Please also carefully clarify the rationale of other formulas.

Figure 3: Should “episodes” be a better word compared to “days” for the y-axis, since the variables plotted are “days”?

The expression needs to be improved, and the use of words needs re-consideration. For example, what does “quasi-saturation” stand for? Please revise “Same as Figure 5 except for YRD” to “Same as Figure 5 but for YRD”. Please carefully check others.

Figure quality can be improved, by increasing resolution and avoiding contours on shadings if both represent the same variable (Figs 5-6)

The authors explore the potential mechanism driving the observed increase in surface Ozone during 2015-2020 over three megacity clusters in eastern China. Observational data for several pollutants from the Chinese National Environmental Ministry of Environmental Protection and Tracking Air Pollution in China dataset are analyzed in the paper to explore the trends in surface ozone over the study regions for the period of interest. Further, reanalysis data from ERA5, NCEP and NCAR are used to investigate the correlation between the evolving weather systems and the positive ozone trends. The study approach is mainly based on statistical analysis of observational data.

General comments:

The paper presents the meteorological conditions conducive to ozone formation (e.g., increased solar radiation) as potentially driving the positive ozone trends, rather than an increase in the anthropogenic emissions or a combination of both. While this is an important and interesting topic, some concerns need to be addressed in the paper before publication. The paper section structure, wording and logic, and the overall presentation can be further improved. More information needs to be included in the paper to further support the hypothesis that weather systems and changing meteorological conditions are responsible for observed increase in O3. Please consider the following suggestions:

• List the processes (e.g., photochemistry) and precursors involved in the production of ozone, explicitly with tables and/or graphs (e.g., EKMA ozone isopleth diagram). Explicitly show the correlation (even if a weak correlation) between emissions of precursors (e.g., NOx, VOCs) and O₃ levels.
• Compare the meteorological conditions to longer range time periods to clearly demonstrate that conditions have evolved towards increased ozone production. Comment on why these conditions have changed.  
• Include information on how land use/development was changed during the same period, to compare against the spatial expansion of high O₃ from urban centers to surrounding regions (past vs current).

Authors acknowledge that their results are mainly based on statistical correlations and further investigation into causal relationships is needed, perhaps with a use of a chemical/transport model. I agree and I’d like to emphasize that this topic is a great case for a model-based investigation, although it might be out of scope for the current manuscript. Model scenario simulations, with different input emissions and for various meteorological conditions, are crucial for further investigating this topic. All models have limitations, but their power and capability in investigating air quality and transport scenarios cannot be dismissed.  

Specific comments:

Line 23: “These favorable meteorological conditions greatly facilitated the formation of O3” -  suggests causal relationship, while only correlation is established in the paper…, please consider revising.

Line 37: “The concentrations of air pollutants SO₂, NOx, CO, PM10 and PM2.5 in China have been significantly reduced since 2013” – what about VOCs?

Figure 1,2,3,4: What caused the reduction in 2020? The Covid19 pandemic related closures and slowed down activities perhaps? You can see the same reduction in Figures 2, 3 and 4. Is this related to decreased emissions of precursors in 2019-2020 or changing meteorological conditions?

Line 85: “time interval of 1 h” do you mean a temporal resolution of 1 h, or your data is for only a 1 hour interval?

Line 93: “duration of O3 pollution,” please elaborate.

Line 93: “can be divided into consecutive O3-exceeding days with four or more days…” - please explain why this particular division was used?

Line 117: The decrease in 2020 suggests correlation with emissions…

Line 118: For completeness, please define “p” before use.

Line 127: “Is it due to changing O₃ photochemical processes or changing meteorological parameters?” – still the big question!

Line 137: How does this expansion correlate with the expansion of urban/industrial regions to not previously developed regions (perhaps industries were relocated to surrounding regions from urban centerers?)

Lines 141 to 144: Why compare the entire 2015-2020 period for high O₃ stations to the sub-period 2015-2017 for low O₃ stations?

Line 144: “quasi-saturation” – define.

Line 145: “approximately 100 ppb” - what are the instrumental/measurement limitations for these sites?

Line 147: “Did it have anything to do with the increase of consecutive O₃-exceeding days” – correlation!

Line 154: “expanded by about a factor of five from 2015 to 2017. ” - how did the land use/development change during this period?

Line 155: how big in area is the BTH box? How does it compare to the resolution of the data you analyzed?

Line 156: “66.42 ppb in2015 (31 days, Fig. 5a) to 69.44 in 2017 (62 days, Fig. 5b)” - comparing averages over different time periods (and number of days), how do you justify this comparison?

Line 159: Not clear what the equation represents. Consider labeling with variables and defining the equation prior to usage….

Line 162: “driven primarily by the increase of consecutive O3-exceeding days” – correlation!

Line 163: “a lion’s share…” – consider revising the wording!

Line 165: What do you mean by quasi-saturation? How does it work? What is the mechanism preventing further increase in concentrations? Related to measurement limitations at the stations?

Line 166: “suggested that there was a quasi-saturation of O₃ inside Beijing City, and an expansion of weather systems conducive to O₃ formation from Beijing toward the southwest of the BTH box during 2017” - So the weather systems conducive to O₃ formation were previously focused on urban centers and now it has expanded to surrounding regions?! Please comment.

Line 177: “Since it is highly unlikely” – please explain with data (e.g., emissions, land use/development) why it is highly unlikely!

Line 249: “into two groups” – why these two groups?

Line 327: include full forms in the section titles rather than acronyms.

Line 320-323: So, the increased ozone is due to reduction in removing processes (low advection and low mixing) while the production is the same and not increased?

Line 343: “… in the former” - do you mean O₃ exceeding days?

Please comment on your choice for compare the average conditions over (for example) 31 days for O₃ exceeding days to average over 152 clear days? What happens if you compare O₃ exceeding days to average over the entire period including both clear days and O₃ exceeding days?

Fig 12: what does the concentration in ppb in parentheses refer to? Figure details are not clear (e.g., isoline labels are hard to read, 5880 gpm is not even labeled)

Technical corrections:

I suggest using different markers in Figures 1,2, and 4, so that different curves are discernible on a grayscale (black and white) version of your manuscript.

Table 3, 4: write complete captions rather than “same as…”

Figures 5,6: complete the captions.

Line 478 and 492: hyperlinks don’t work, please check!