The authors applied a relatively new kind of artificial intelligence to examine marine heat waves (MHWs) in the Mediterranean Sea.  They highlight both the strengths (ability to issue reasonable forecasts) and weaknesses (e.g. computational intensity) of the method.  There are some minor revisions and comments to address, but I recommend this manuscript for publication.



One significant addition, preferably in the paragraph starting on line 137, would be to note the important fact that MHW is defined based on a 30-year climatological mean.  With most SST data sets now going back to the 1980s, the selection of a 30-year window can alter the results of an experiment like this, especially since many regions have statistically significant different temperatures now compared to 30-40 years ago.  Which 30 year window was used for the identification of MHWs, or is it based on the window 1982-2017 like the SST anomalies?



It appears that the AI model used is fairly useful for short-term forecasts.  Could you please note the merits of this relative to other AI methods used in climatology?



Line 30: Generally we use ^oC/decade or ^oC/century.

Line 50: "ML" needs to be defined here.

Line 87: "Artificial Neural Network" needs no capitalization

Fig. 1: Ierapetra Gyre is labeled with both an arrow and one of the red rings

Line 155: "SSTA" needs to be defined here.

Fig. 2: is the domain 128x272 the size of the data region in terms of data matrix size?

Eq. 2: y and γ are used interchangeably, as are a and α

I found your study very interesting and relevant to the growing field of AI-driven forecasting for extreme ocean events. Below are some comments that I believe could strengthen your manuscript:

1. Comparison with ocean models: While you acknowledge ocean models in the introduction, there is no direct comparison between your AI-based approach and a traditional numerical ocean model for MHW forecasting. A quantitative comparison—whether in terms of forecast accuracy, computational efficiency, or ability to capture physical processes—would provide valuable context and help clarify how AI complements or improves upon traditional approaches.
2. Resolution of the output: You mention that the final spatial resolution is downsampled to a 128x272 grid, but it would be helpful to explicitly state the corresponding resolution in kilometers. Additionally, how does the downsampling affect the model’s ability to capture finer-scale MHW dynamics, particularly in regions with complex topography such as the Adriatic or Aegean Seas?
3. Uncertainty Quantification: Your study provides a robust evaluation of model performance, but there is limited discussion on uncertainty quantification. Given the stochastic nature of neural networks, have you assessed the sensitivity of your predictions to different training datasets, hyperparameter choices, or initial conditions? Methods such as ensemble modeling could provide insights into the confidence of the forecasts.
4. Generalization Beyond the Mediterranean: You mention that the proposed method could be applied to other regions, but it would be helpful to discuss potential challenges in doing so. For example, would a model trained on Mediterranean SST anomalies generalize well to other basins with different oceanographic characteristics (e.g., stronger currents, different stratification, or more extreme variability)? A brief discussion of how the model could be adapted or retrained for different environments would be valuable.

Overall, this study presents a promising application of deep learning for MHW forecasting, and I appreciate the detailed methodology and validation process. Addressing these points could further enhance the robustness and impact of your work.