| 23 Jun 2026
Meteorological normalization of surface ozone variability across warm and cold seasons in China during 2015–2024
Abstract. Surface ozone (O3) concentrations in China have continued to increase despite substantial reductions in primary air pollutants, but the relative roles of meteorological variability and longer-term non-meteorological changes remain uncertain across seasons and regions. Here, we applied a LightGBM-based meteorological normalization framework combined with SHapley Additive exPlanations (SHAP) to investigate interannual variations in maximum daily 8 h average O3 (MDA8 O3) across China during warm and cold seasons from 2015 to 2024. The model reproduced daily MDA8 O3 variability reasonably well, with mean testing R2 values of 0.71 and 0.77 in the warm and cold seasons, respectively. Observed national mean MDA8 O3 increased by 2.1 μg m-3 yr-1 in the warm season and 1.7 μg m-3 yr-1 in the cold season. After meteorological normalization, MDA8 O3 still increased at 1.2 μg m-3 yr-1 in both seasons, indicating that the decadal O3 increase was mainly associated with non-meteorological components. SHAP analysis revealed distinct seasonal and regional meteorological associations. During the warm season, temperature and solar radiation were more important in northern inland and basin regions, whereas relative humidity and wind-field variables were more important in southern coastal regions. During the cold season, solar radiation dominated across most regions, while relative humidity was more important in the Pearl River Delta. The net meteorological contribution shifted from generally negative during 2015–2019 to positive after 2019, indicating that the recent unfavorable meteorological conditions have amplified O3 pollution in several regions.
General comments
This manuscript applies LightGBM-based meteorological normalization and SHAP analysis to investigate warm- and cold-season ozone variability across China during 2015–2024. The national-scale dataset and direct comparison between the two seasons are valuable, and the results may provide useful information for ozone management. However, the main quantitative interpretation is still not sufficiently supported by the analysis results. I think major revision is required before the manuscript can be considered for publication.
Specific comments
Chapter 2: Details about the methodology are required. Please clarify the variables used to calculate the KNN interpolation (maximum gap length, proportion of imputed observations by site and year,…), MDA8O3 (the minimum number of valid hourly values, …), etc…. Also, the detailed setup of hyperparameters for LightGBM and for SHAP should be described.
Line 128-: Unix time was included as a predictor to represent long-term changes and was kept fixed during meteorological resampling. However, long-term changes in temperature, radiation, humidity, or other meteorological variables can also be correlated with Unix time. Therefore, the model may assign part of the meteorological trend to the temporal predictor, and the normalized component is not necessarily a uniquely identified non-meteorological signal. I think sensitivity tests excluding Unix time, or using alternative representations of the long-term trend, are necessary before concluding that non-meteorological changes were dominant.
Line 133-: The daily dataset was randomly divided into training and testing subsets at an 80:20 ratio. However, ozone and meteorological variables have strong temporal autocorrelation, so adjacent days or the same pollution episode can be included in both datasets. This may overestimate the model’s performance. I recommend using cross-validation based on selected time series blocks (not randomly).
Lines 270-: The cold season is defined as November–April, which combines winter with the spring. Because ozone can increase rapidly during March and April, especially in southern China, the reported cold-season trend may be mainly driven by springtime changes. Please provide monthly normalized trends or at least separate November–February and March–April analyses.
Lines 307-: The meteorological contribution defined by the normalization method, O−N, and the annual sum of meteorological SHAP values are not necessarily the same quantity because they are calculated relative to different baselines. I think these two estimates should be directly compared for each year and region. Without this comparison, the net meteorological contributions in Figs. 6b and 7b may be interpreted as equivalent to the decomposition in Fig. 5, although this equivalence is not guaranteed.
Lines 362-: Mechanistic interpretations are stronger than the presented analysis can support. Direct NOx/VOC observations and transport or chemistry diagnostics were not included. I think these should be clearly presented as hypotheses unless they are supported with additional chemical observations or process-based modeling.