• In the manuscript titled “Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework ”, the authors develop a new model evaluation package to systematically diagnose atmospheric river (AR) biases. The characteristic of this tool is the robust response to structural differences of AR detectors. So this tool is designed to intercompare the ARs as simulated by multiple climate models. There are still certain changes and clarifications that the authors should address prior to publication. For these reasons, I believe that the manuscript can be accepted for publication by the GMD after minor revision. Below, I have some general suggestions to the authors.

   

  General comments:

  • The paper appears to be rushed, with inadequate detail and poor organization, and it seems that it was not carefully reviewed after completion. For instance, the introduction lacks systematic coverage and fails to logically present the structure of the package, which is contrary to the reader’s expectations. The crucial code section is that "full environment and python packages include AR metrics," it would be hard to find which section of PMP works for AR evaluation. The writing is not standardized, requiring readers to search backwards/proof/numbers for clarification. For instance, in Section 3, although the topic is metrics, specific numbers (metrics results) are not mentioned frequently, leading to a general lack of data support for qualitative analysis. Readers have to compare data themselves. Therefore, it is recommended to revise the writing based on similar highly-cited papers in AR evaluation.

   

  Specific comments:

  • Line #93, the introduction of the paper should not include information just for the sake of writing an introduction. This statement is not an actual argument; it is unnecessary and does not require citation of references to support it. This entire section covers very basic common knowledge and should be removed. The metrics are extremely common and there's no need to list them.

   

  • Line #144-145, the selected model (E3SM-HR, E3SM-LR) should include some basic tabulated information about its parameters, including grid resolution.

   

  • Line #178, considering the target audience of the paper and the need for conciseness in scientific writing, the section introducing the computational methods should not be overly detailed. Lines 178 to 196 should be summarized in a few sentences. The same issue also appears in the pattern correlation section (lines #205-#214).

   

  • Line #235, could the author list the percentage next to this range? The proportion of the pattern variance that the principal components can explain should be quite low for 95%, such as first or second PC, etc. Additionally, within what range was this data calculated?

   

  • Line #272, what is the frequency of the data on which this calculation is based? Monthly?

   

  • Figure 2, the color scale intensity and the magnitude of the numbers are inversely related. I suggest adjusting the scale to consistently increase. Unless all values pass the significance test and are noted in the figure caption, additional markers, such as an asterisk next to the numbers, should be added to indicate significance test results.

   

  • Line #276, Fig.2 needs to be described, such as what percentage of models in a specific ocean area have correlations that pass the significance test, using numbers to support the qualitative description.

   

  • Figure 3, this figure is missing subplot labels like (a), (b), etc. The scale for the difference should be placed at the bottom to avoid confusion.

   

  • Line #280, the spatial pattern correlation in S. Pacific is 0.88, and N. Atlantic is 0.98, these numbers in Fig.2 and the spatial gradient in Fig.3 should be discussed to support this statement. Additionally, just a suggestion—why not choose graphs with larger differences for comparison, such as BCC (0.99) and IPSL-CM5A (0.82) in the Indian Ocean? The comparison would be more intuitive given the same study area. Line #290 could be updated into “be better interpreted together with AR frequency maps with spatial gradient”.

   

  • Figure 4, the hatching obscures the colors; it's recommended to place asterisks next to the numbers or bold the numbers to replace the hatching. This is just a suggestion, in Figure 4, such comparisons could benefit from providing a global ocean basin average shape for each model, which would make deviations in latitude and longitude more intuitive.

   

  • It is a suggestion only. Regarding Fig. S1, why not interpolate or downscale to the same resolution before comparison? It will still prove the difference in original data resolution. Different grid resolutions will inevitably introduce boundary issues. Fig. S1, the default color scheme makes the land boundaries unclear and needs to be adjusted. Fig. S2, the specific meanings of ARCONNECT and TECA are unclear.

   

  • Figure 5, please consider that those with red-green color blindness may have difficulty distinguishing between these two colors.

   

  • Line #325-#330, could you provide some explanation in this section of the results? For example, the characteristics of the models?

   

  • Line #379-#383, Here, it would be better to explain how the different thresholds for tagging the moisture field contribute to the differences in AR shapes.

   

  Language issues:

   

  • Line #78, the abbreviation ARDTs should be defined the first time it appears.

   

  • Line #120, the abbreviation ARDT should be defined the first time it appears.

   

  • Line #124, the TE ARDT should be defined the first time it appears?

   

  • Line #130, “Mundhenk_v3 tags” could be replaced with “fixed-relative (Mundhenk_v3 tags)”

   

  • Line #136, in general, the full name of a climate model abbreviation should be provided the first time it appears.

This paper presents a suite of new atmospheric river (AR) metrics that are designed for quick analysis of AR characteristics and statistics in gridded climate datasets such as model output and reanalysis. The study is very interesting, well organized and written. This work could be published if the following comments are adequately addressed. The author should show more convince evidences for their method robust. For example, for figure 3, what about AR frequency in the South Pacific for BCCCSM2-MR and ERA5, and their differences? What about AR frequency in the North Atlantic for CSIRO-MK3-6-0 and ERA5, and their differences? In addition, what is the advantage of the present method, compared with others’? This should be discussed.