The paper is in general well written and well structured. It provides interesting findings about transferring a concept of a machine learning based correction scheme of subgrid source terms from the FV3GFS general circulation model to the EAMxx general circulation model. The content of this paper is helpful for the modeling community as it clearly states encountered problems of such a migration. Despite this I recommend the authors to expand the interpretation of the results of their hybrid simulations. Moreover all figures require an improved description what is shown in the respective captions. Likewise I strongly recommend to the authors to adjust their way how they cite data or software used to fulfill the standards of Copernicus journals.

As a result I recommend major revision of this submitted manuscript. Attached you will find some specific comments and a detailed list of technical comments with respective line numbers in the submitted manuscript.

The article presents work with the aim to use machine learning to nudge a coarse version of the EAMxx-SCREAM model towards the results of a high resolution run with the goal to obtain results corresponding to high-res runs at significantly lower computational costs. For this, the authors follow previous work with another Earth system model, FV3, and use the openly available algorithms that were successfully implemented for FV3. However, in the present article, the results of the nudging were less promising than in the previous work. The authors speculate as to why this is the case. They also argue that, despite the less effective nudging, there are additional benefits stemming from the model developments made, such as Python to C++ bridging added to the model which will also be beneficial for other developments.

In general, this is an interesting and highly timely work that should be published. I also feel very favorable to publishing work that has not proven entirely as successful as initially hoped. However, in the present form, the article feels incomplete and lacks motivation and discussion and thorough investigation as to why the work was not as effective as for the FV3 model, which would make the article useful for readers attempting similar endeavors. I therefore recommend major revisions as detailed in the attachment.