Thank you for your detailed and insightful comments on our manuscript. We have addressed all of your concerns and questions in the attached PDF. In the document, your review comments are displayed in black text, and our responses and corresponding revisions are highlighted in red. Once again, we sincerely appreciate your valuable feedback.

We sincerely appreciate the time and effort you have dedicated to reviewing our manuscript and providing valuable feedback. We are deeply sorry that our initial submission did not meet your expectations in certain areas. Your insightful comments are of great importance to us, and we have carefully considered each point raised. Below, we provide a detailed response to your concerns:

（1）On the novelty of our methodology

We value your comments regarding the novelty of our study and have reviewed the references you mentioned (Lo et al., 2018; Shokouhi et al., 2024; Huete et al., 2019). While acknowledging the contributions of these studies, we believe our work has distinct differences:

Lo et al. (2018) developed pollen calendars for North America using data from 31 National Allergy Bureau stations, focusing on analyzing the start, end, and duration of pollen seasons and annual pollen integrals for various taxa. In contrast, our study centers on developing a phenological model specifically tailored to grass pollen in the autumn season. Using temperature and sunshine hours (SSH) as inputs, our model can accurately predict the start and end dates of pollen seasons not only for the current year but also for future scenarios when meteorological forecasts are available. This approach moves beyond data analysis of existing records to establish a predictive framework for grass pollen phenology.

Shokouhi et al. (2024) and Huete et al. (2019) employed machine learning algorithms to directly simulate pollen concentrations, yielding notable results. In our study, however, the random forest (RF) model was applied solely to simulate annual pollen production due to its effectiveness in handling the numerous factors influencing this variable. Unlike these studies, RF represents only one component of our broader modeling framework, rather than the primary focus of our methodology.

In conclusion, we believe our study presents clear novelty and fundamental differences compared to previous research. By combining phenological model tailored for grass pollen with predictive capabilities and integrating it into a broader modeling framework, our work goes beyond the methods used in earlier studies. This approach not only advances our understanding of pollen dynamics but also provides tools for future forecasting under changing climate conditions, demonstrating its distinctiveness and innovation.

（2）On the spatial resolution of plant functional type (PFT) data

We greatly appreciate your insightful comments regarding the importance of PFT data and its resolution requirements. We fully agree that accurate characterization of pollen emission sources requires careful consideration of PFT spatial heterogeneity, particularly the partitioning of grasses into C3 and C4 components. Although the 5-km resolution PFT dataset provided by NCAR CLM may not be highly detailed, we believe it is sufficient for the scope of this study, given that Beijing, as a major metropolitan area in China, spans approximately 16,411 km². Moreover, for the purpose of this study, which focuses on developing and testing a pollen emission potential model, we believe that this resolution is sufficient to address the core research objectives.

Additionally, we understand that concerns about insufficient spatial heterogeneity in PFT data, combined with issues regarding figure quality, may stem from the spatial resolution of our final pollen emission model output. In our study, the model was applied at a 0.1° (~10 km) resolution across the Beijing area. We recognize that this resolution might appear coarse, but this decision was made to balance the trade-off between computational efficiency and model complexity. Simulating pollen emissions at higher resolutions would significantly increase computational demands, especially when using interpolated meteorological data and random forest algorithms for regional modeling.

That said, we agree with your suggestion to consider finer-scale PFT data and higher-resolution pollen emission modeling in future studies. Such efforts will allow us to better capture the spatial variability of pollen sources and improve the accuracy of our predictions.

（3）On the quality of figures

We sincerely apologize for the shortcomings in the quality and clarity of the figures in our original submission. We understand that figures are a crucial element for effectively communicating results. In response to your feedback, we have thoroughly reviewed and revised all figure captions to enhance their clarity and informativeness. Specifically, for the figure you mentioned (Figure 11, now revised as Figure 7), we have redesigned the layout and provided a detailed caption explaining the rationale for using two y-axes with different scales. These changes aim to eliminate any potential confusion for readers. We have also conducted a comprehensive review of all other figures to ensure they meet high standards of clarity and presentation.

Once again, we deeply appreciate your constructive feedback, which has been instrumental in helping us improve our work. We hope that the revised manuscript will better address your concerns and meet the expectations of ACP. If you have any additional comments or suggestions, we would be most grateful to receive them.

Thank you for your understanding and consideration.