See attached PDF. 

The study presents an extension of Dynamic Mode Decomposition (DMD) by incorporating a regularization term specifically designed for extreme events, thereby enhancing the reconstruction accuracy of extreme climate phenomena. The proposed method successfully identifies extreme event mode that facilitate targeted analysis of extreme events, offering both scientific and practical value. However, the following aspects require further refinement to strengthen the manuscript:

Detailed Recommendations​​

The Abstract could include quantitative comparisons (e.g., relative error reduction) of extreme event reconstruction performance between the proposed method and standard DMD.

The Conclusion section requires refinement to clearly delineate and distinguish the novel contributions from existing theoretical frameworks.We can delineate three novel contributions: a) Regularization design: integration of an extreme-event penalty term into the DMD objective function. b) Mode selection: Automated identification of extreme-relevant mode. c)  Discovered climate science insight.

While the manuscript mentions sensitivity analysis regarding the selection of rank values, it does not present the corresponding results or their implications.

A comprehensive sensitivity analysis could be conducted to validate the rationality of extreme event selection criteria. Additionally, I’m curious about whether the selection ofextreme events outside the modal space would impact the results.

The physical interpretation of the identified modes needs deeper exploration, particularly through integration with atmospheric dynamics principles to substantiate the selection of extreme event modes.Is it overlooked in previous studies? If not, we should add some references.

(Optional)  Improving code and data accessibility would enhance research reproducibility and academic exchange, aligning with current academic best practices.