This is a well-written and structured manuscript, easy to read and follow meanwhile without losing the rigour of scientific work. It deals with the evaluation of atmospheric models for simulating solar and wind power in California, with multiple layers of investigation, i.e., evaluating the impact of using different atmospheric models, spatial resolutions, and solar and wind power generation models. Answering these questions would help users to understand the impact of different factors on solar and wind power calculations, and to decide on the best solution given the available resources. This is particularly important in the context of the current global transition toward a carbon-neutral energy system. I believe this manuscript could be further improved by addressing the points listed below:

1. The necessity or irreplaceability of the work needs to be sharpened. What makes it so important to evaluate solar and wind power generation derived from the atmospheric models (or meteorological data sets) you mentioned in California? What are their special things compared with other alternatives, or can they represent the others?
2. Adding information about the status quo of renewable energy installation in California and how much they cover the local electricity demand may help readers further understand the importance of your work.
3. The limitations of this study should be uncovered and discussed in the manuscript.
4. The conclusions you have made based on the results in the work, are they applicable to other regions? Or, are they applicable to other models?
5. Are monthly CF observations really able to identify the difference of the high- and coarse-resolution atmospheric modeling outputs by evaluating their derived power results? The fluctuations in renewables, especially in wind, have been largely filtered out at the monthly scale, for which high-resolution models are good at solving while coarse-resolution models are not. Would not evaluating the direct solar and wind output variables from atmospheric models against observations from, e.g., synoptic stations and weather masts, make more sense for this purpose? Since hourly or finer time scale CF are not reachable as mentioned in the manuscript.
6. Please justify in this manuscript why this work chose to use grid-cell (figure 3, 4, 5, 8; 11, 12) based evaluation, instead of evaluating over every plant (figure 9; 15) all the time?
7. The study indicated in line 99 that the only difference between SCREAM-3kmCARRM and SCREAM-800mCARRM is the horizontal resolution, and in line 100-101 it also indicated that the physical parameterization is sensitive to horizontal resolution. Then, why do you think using the same suit of physical parameterization in these two experiments is fair and reasonable?
8. Since this work recognized the effect of using different loss schemes in the generation estimate models, it should disclose the details of the wake scheme used in the PySAM/SAM, even though it is the default one. A brief intro as you did to PLUSWIND in lines 263-264 would do the job for PySAM/SAM. A great solution would also be adding words justifying why a certain wake scheme is chosen to be used in the workflow.

Now follows some technical comments:

• Make sure every acronym mentioned in the abstract has its full name explained there (e.g., SCREAM-RRM is a bit surprised to me)
• To be precise with the used language, solar and wind power are not renewable technologies, wind turbines and solar PV systems are. Solar and wind power are renewable energy sources. Please reformulate line 23 accordingly, and check throughout the manuscript to get rid of similar errors.
• In line 97 the study used “water years”, which is a field-specific terminology. It is suggested to explain its meaning at its first occurrence (line 97, while its first explanation now is in line 172) or use a more common term like weather years.
• The headline index might be wrong in section 2.1 and 2.2. Now it appears nothing presented under the headline 2.1, which should not be the case.
• Please check the entire Methods section, many acronyms mentioned without introducing its full name at the 1st occurrence, such as SVD, MPAS, HICCUP
• Line 216, repetitive to line 114-115, please reformulate and avoid using the exact same sentence
• Line 274, repetitive word used “preprocessing” 
• Please add references to underpin your perspectives in line 315-316
• Figure 3: suggest to rearrange the legend into multiple columns (like 3) to save space, and to add index for subplots like a), b), c), which applies to other figures as well in this manuscript
• Figure 4: suggest to keep legend only one time for one row since they are the same, the same applies to Figure 5 and other figures from solar
• Line 423: I don’t see an underestimation in SoCal from HRRR, at least not from the bold line. Also it seems to me that NorCal has the least discrepancy present instead of SoCal.
• Figure 6:  to save space, suggest to use a shared colorbar, put repetitive names as row name and column name, and show latitudes and longitudes only at the first column and the last row, the same applies to other figures from solar, otherwise some numbers are hard to read at the moment in these figures
• Figure 7: texts are hard to recognize, save space by reducing repetitive information as suggested before, try to use a landscape layout or split them into two panels to improve the visualization
• Figure 14: why not showing the “difference” subplots you have in Figure 7?
• Figure 9 and 15: try to adjust the legend so that it would not overlap the plot

Please see the attached file for comments on egusphere-2025-3947.