This study presents a convolutional neural network (CNN) framework—TCNN v1.0—for retrieving key tropical cyclone (TC) intensity and structure metrics, such as maximum sustained wind speed (VMAX), minimum central pressure (PMIN), and radius of maximum wind (RMW), from gridded climate data. A major strength of this framework lies in its ability to infer realistic TC intensity characteristics from relatively coarse-resolution reanalysis (MERRA-2), addressing a long-standing challenge in global climate models where TC structures are typically under-resolved. The authors argue that this approach has the potential to improve TC intensity estimation from both current numerical weather predictions and future climate model outputs.

The study includes a thorough analysis of model sensitivity to input variables, domain configuration, and especially data sampling strategies. The results underscore the importance of proper train-test data partitioning, as the model’s performance degrades substantially when tested on unseen TCs using a chronological split. This finding is important and well-motivated. However, if the generalization issue is one of the study’s key conclusions, the decision to report the model’s primary performance metrics based on random sampling (where samples from the same TC may appear in both training and test sets) needs further justification. Specifically, while the reported RMSE for VMAX prediction (7.11 kt) appears to outperform previous methods, this result may overestimate the model's actual predictive capability, as the RMSE increases to 19.2 kt under a more realistic chronological split.

Furthermore, the authors cite existing studies such as Chen et al. (2019), which also employ CNN-based approaches to retrieve TC intensity from satellite data. Since Chen et al. used a chronological split in their validation, a more direct and critical comparison would be appropriate, even if the architectures and input data sources differ, especially given the common goal of improving TC intensity retrieval.

These issues also call into question the core assumption of the study—that ambient environmental conditions at 0.5° resolution contain sufficient information to estimate TC intensity. If the model struggles to generalize to new TCs, this may suggest that it is learning TC-specific patterns rather than robust physical relationships. As this assumption is foundational to the study’s broader claims, especially regarding the potential application to future climate projections, further justification or clarification is needed.

The sensitivity test on domain size (Section 3.2.1) is informative, and the conclusion that a 25°×25° input domain yields the best performance is reasonable. Still, more discussion linking the domain size results with those from model architecture and convolutional kernel experiments would strengthen the study. This would also help clarify how spatial context is encoded and used by the CNN. Similarly, the reported seasonal variation in TCNN performance deserves more physical interpretation, particularly regarding how environmental influences on TC intensity may vary by season.

In summary, while this study presents an innovative and potentially valuable approach for estimating TC intensity and structure from gridded climate data, the current manuscript does not yet provide sufficient justification for its core claims. The reliance on a data sampling strategy that inflates performance metrics, coupled with limited generalization to unseen TCs, raises concerns about the framework’s robustness and applicability, particularly for future climate projections, which inherently involve unseen conditions. Furthermore, the key physical assumptions underlying the model are not adequately supported by the results, and the sensitivity analyses, while informative, could be more cohesively interpreted to strengthen the physical insights.