Reviewer #1

General comments:

This study employs machine learning techniques to investigate the patterns and driving factors underlying the fluctuations in isoprene levels — a crucial precursor to surface ozone — using extensive historical datasets. The analysis demonstrates good agreement with previous short-term modeling results, while also emphasizing that vegetation expansion and temperature increases linked to climate change are key drivers of long-term variability. Moreover, by extending the model projections to the year 2100 and integrating them with a detailed chemical box model, the researchers estimated future surface ozone levels. Their results indicate a significant rise in ozone concentrations if NO_x emissions are not effectively controlled. Importantly, these conclusions were obtained using a data-driven approach that differs from conventional atmospheric chemical transport models, highlighting the robustness and novelty of the findings. I recommend acceptance for publication in ACP after minor revisions.

Response: We sincerely thank the reviewer for the positive and encouraging evaluation of our work. We appreciate the recognition of the study’s novelty, the robustness of the data-driven framework, and the relevance of our findings regarding long-term isoprene variability and its impactions for ozone (O₃). We have carefully addressed all issues raised by the reviewer and revised the manuscript accordingly. We believe that these changes have further improved the clarity and quality of the paper. Below, we provide the point-by-point responses to each comment, with our replies highlighted in blue and the corresponding revisions marked in red.

Please kindly find the attached PDF-file for our responses.

Reviewer #2

General comments:

The manuscript presents an explainable deep transfer learning framework (PINN-ResMLP) to predict urban isoprene concentrations and attribute their variability across Chinese and international cities. It further explores long-term drivers in Hong Kong and London (1990–2023) and projects future isoprene and ozone responses under CMIP6/SSP scenarios, including NO_x-control sensitivity. The study fills an important gap: robust isoprene prediction without detailed local emission inventories or explicit chemistry, and interpretable attribution that links meteorology, greenspace, and traffic to observed and modeled trends. The approach is timely and impactful for urban air quality management in a warming climate. The integration of physics-informed constraints with transfer learning is a notable strength, as is the explicit discuss ability (SHAP-based) of model predictions. The Hong Kong–London contrast is compelling and policy-relevant.

Response: We sincerely thank Reviewer #2 for the thorough and positive evaluation of our work. We greatly appreciate the recognition of the novelty and impact of our study, particularly the explainable deep transfer learning framework (PINN-ResMLP), its ability to predict isoprene concentrations without detailed local emission inventories, and the interpretability provided by SHAP analysis. We also thank the reviewer for emphasizing the relevance of the Hong Kong–London comparison and the importance of integrating physics-informed constraints with transfer learning. The constructive feedback and supportive comments are highly encouraging and have helped us further clarify and refine the manuscript. Below, we provide our point-by-point responses to each comment, with our replies highlighted in blue and the corresponding revisions marked in red.

Please kindly find the attached PDF-file for our detailed responses and revisions.