the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jul 2025
A 23-Year Nationwide Study Revealing Aerosol-Driven Light Rain Shifts in China's Emission Control Era
Abstract. Precipitation dynamics critically regulate Earth's hydrological cycle and climate system, yet the mechanisms driving decadal-scale variations in light rain remain poorly quantified. Our analysis of a 23-year (2000–2022) national-scale dataset reveals contrasting trends in light precipitation occurrence: a significant decline (1.0 days yr⁻¹, p < 0.05) during 2000–2013 followed by a pronounced increase (1.9 days yr⁻¹, p < 0.01) in 2013–2022. Cross-temporal analysis demonstrates a national wide inverse correlation (r = -0.55, p < 0.01) between aerosol concentrations and light rain frequency in the China’s Emission Control Era, when the PM2.5 shows an upward trajectory before 2013 followed by a markedly downward decline thereafter, providing a natural experiment to quantify aerosol effects in precipitation. Through multi-algorithm machine learning and causal inference modeling, we further identify aerosol-cloud microphysical processes as the dominant driver, with PM2.5 concentration changes explaining 59–63 % of the decadal trends of light rain. As a result, the PM2.5 reduction (increase) enhances (reduces) light rain frequency by +1.97 (-2.08) days yr⁻¹. Meteorological factors showed negligible temporal variability and thus insignificant explanatory power (<10 % for each individual factor) over a decadal scale. Our findings establish, for the first time, the quantifiable aerosol microphysical effect on light precipitation trends, highlighting dual benefits for China's emission control policies that PM2.5 reduction in 2013–2022 simultaneously enhanced light rain frequency while improving air quality. This work offers critical insights for aligning air pollution mitigation with climate adaptation strategies.
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Status: open (until 02 Sep 2025)
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RC1: 'Comment on egusphere-2025-2472', Anonymous Referee #3, 13 Aug 2025
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This study analyzes 23 years (2000-2022) of nationwide data in China, revealing a decline in light rain from 2000-2013, followed by an increase from 2013-2022. The shift closely aligns with PM2.5 trends during China’s Emission Control Era, with machine learning and causal inference showing that aerosol-cloud microphysical effects explain 59-63% of these decadal changes. This work bridges atmospheric chemistry and hydrology by combining long-term data and advanced analysis to separate human-caused aerosol effects from natural variability, filling a major knowledge gap and providing policy-relevant insights for aligning air pollution mitigation with climate adaptation strategies. Overall, the paper is well-written with logical organizations. However, the paper still has some unclear or incomplete parts need to be improved before the publication.
- Justification for 2013 as a turning point
The study designates 2013 as the dividing year for trend analyses in both precipitation and aerosol concentrations but offers insufficient background or rationale for this choice. Using the same breakpoint for both variables without justifications risks introducing bias, particularly given that the XGBoost model subsequently identifies aerosols as the dominant factor. The authors should provide a robust justification, supported by literature, independent evidence, or an objective determination from precipitation data, explaining why 2013 is also an appropriate breakpoint for light-rain analysis.
- Justification for Regional Division
The authors divide the study area into six regions without sufficient justification. For example, it is unclear to me why regions with similar light rain frequencies and trends are treated separately rather than combined (e.g., FW and NC). Clarification on the criteria or rationale behind the regional boundaries is needed.
- Justification of Selected Factors in XGBoost Model
Little explanation is provided for focusing solely on PM2.5, RH, WS, T, E, TCLW, CAPE, and LCC as factors explaining light rain trends. It remains unclear whether other relevant variables were considered or excluded. I recommend the authors provide evidence or rationale supporting the selection of these factors to demonstrate that the analysis covers the most important influences.
Following are some specific comments.
Specific comments:
- Abstract: Some results like +1.97(-2.08) and 59-63% cannot be found in the main text. Please make sure the consistency.
- Line 98-100: no literature or data to support the opposite trends before and after 2013.
- Figure 1: It’s good to show the regions have significant trends or correlation, like Figure 3. In addition, please use symmetric color bar for middle panel.
- Line 197-200: should be Fig S2 since Fig. S1 does not show correlation results.
- Figure 2: 1) no x-axis information, 2) no caption for the plot in the middle.
- The opposite trends of light rain and aerosol before and after 2013 in different regions are shown in Figure 2. How are trends in national wide scale and how do they apply to other meteorological factors?
- Are the details in Figure S1already plotted in Figure 3? If yes, no need to add Figure S1.
- Figure 4: contribution before and after 2013? Why not as a whole?
- Check throughout the manuscript. Some phrases show repeatedly with both the full term and abbreviation together (e.g., SEM).
- Figure 7: Please consider using more distinctive colors for clearer differentiation.
- Reorganize the section 3: it alternates between discussing nationwide results and those from the six major regions, causing confusion as the narrative and figures jump back and forth.
- Why is the quantification of contributions from different factors divided into two separate periods instead of analyzing the entire 23-year period as a whole? Since the key contributors appear similar for both periods, splitting the analysis may reduce the ability to identify the main drivers of the shifted trends. This choice along with the resulting interpretation seems unclear. Further clarification or justification is recommended.
Citation: https://doi.org/10.5194/egusphere-2025-2472-RC1 -
RC2: 'Comment on egusphere-2025-2472', Anonymous Referee #1, 14 Aug 2025
reply
This manuscript presents a comprehensive nationwide study on the decadal shifts in light rain frequency in China from 2000 to 2022, with a focus on the role of aerosols and meteorological factors. By integrating long-term observational data, multi-algorithm machine learning, and causal inference modeling, the study quantifies the dominant influence of aerosol-cloud microphysical processes on light rain trends, highlighting the dual benefits of China’s emission control policies. The research fills a gap in understanding the mechanisms behind light rain variations in the context of emission reduction, with robust methods and significant implications for aligning air pollution mitigation and climate adaptation. Overall, the findings are valuable and insightful, and I recommend its publication after minor revisions.
Comments
The article mentions the use of 5-fold cross-validation and parameter search space to determine the optimal parameters, but does not specify the value ranges and final selection results of the key parameters (such as learning rate, tree depth, subsampling rate, etc.).
The abstract states that “The variation in PM2.5 concentration explains 59-63% of the interdecadal trend of light rain”. However, in the results section 3.4, it is stated that “Aerosols play a dominant role in driving the long-term trend of light rain precipitation frequency, and contribute 58-65% to the interannual variation of annual light rain days”. There is a difference in the numerical range of the contribution of aerosols to the trend of light rain in these two statements.
Provide the CFI/RMSEA thresholds you used to judge SEM “very good fit” (currently only values are given).
Introduction: The definition of light rain (“daily accumulation between 0.1 and 10 mm”) is cited from Dunkerley (2021), but it is also noted to follow China Meteorological Administration (CMA) standards in Section 2.2. To avoid confusion, clarify the connection between the two (e.g., “consistent with the standards of the China Meteorological Administration (CMA; Dunkerley, 2021)”).
Line 127: “liner regression” is misspelled; revise to “linear regression”.
Line 389: “2000 – 2023” is inconsistent with the study period (2000–2022) mentioned throughout the manuscript; correct to “2000 – 2022”.
Abstract: PM2.5 subscript, please check the entire text.
Line 209-210: “aereas” should be “areas”.
Lines 40: The statement “studies have … based on long-term meteorological and aerosol dataset” requires supporting references.
Figure 7 caption: “Quantified contribution of each individual factor”, “each" and “individual” are redundant; revise to “Quantified contribution of each factor”.
Citation: https://doi.org/10.5194/egusphere-2025-2472-RC2
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