1. The introduction is comprehensive and well supported by relevant literature. However, the overall narrative could be clearer. The progression from CORDEX to CMIP5 and CMIP6 is somewhat non-linear. The distinction between background, previous studies, and the specific contribution of this study is not always clear. As a result, the main focus of the paper, standardized evaluation and model selection for future city-scale downscaling, is not sufficiently emphasized. A clearer structure would improve readability and highlight the key contribution.

2. The description of the study objectives is somewhat unclear. In Section 2.3.1, model evaluation is referred to as the “second objective”, and in Section 2.3.2, model selection is described as the “third objective”. However, the first objective is not explicitly defined. It seems that the construction or documentation of the CMIP6 CORDEX-SEA ensemble (Section 2.1) may correspond to the first objective, but this is not clearly stated. It would help to clearly define all study objectives earlier in the introduction. In addition, the distinction between Sections 2.2 (data) and 2.3.1 (evaluation methodology) could be made clearer to avoid possible overlap.

3. Around line 321, the text refers to Fig. 1 for tas biases, while the results appear to correspond to Fig. 2.

4. The discussion of the seasonal-cycle benchmark over the Maritime Continent is reasonable. However, a brief explanation of why the unimodal benchmark is not applicable in this region would improve clarity. Adding a supporting reference may also help.

Overall, this is a solid and valuable study with clear relevance to regional climate modelling. The methodology is sound and the results are useful, although the presentation and narrative structure could be improved to better highlight the key contributions.

The manuscript describes an ensemble of CMIP6 downscaling simulations and evaluates them to identify a subset that can be further downscaled to city scales for climate impact assessment. As per the GMD Review Criteria, my Scientific Significance score is Excellent (it furnishes a wealth of information on the performance of different regional climate models and their configurations); Scientific Quality is Excellent (the simulations have huge potential for further research leading to significant scientific results); Scientific Reproducibility is Excellent (data and models are sufficiently described and all links in the Code and Data Availability section work); and Presentation Quality is Good (figures are clear, manuscript is well organised, but some descriptions are a little confusing). Overall the manuscript is perfectly suited for GMD and the authors have done a good job evaluating such a large ensemble of data and distilling actionable information from it.

My comments mainly concern the organisation of the manuscript and ensuring clarity in the communication of results, where I found some descriptions confusing. There are also a few topics I would like to see more discussion on. These issues should be straightforward to address and I expect the manuscript can be published thereafter. I recommend major revisions rather than minors because of the number of points I’ve raised.

Major Comments

1. The objectives of the study could be made clearer which aren’t stated up front but referred to throughout the manuscript. In the benchmarking section, line 256 states “the third objective of this study, which is to identify a subset of CMIP6- downscaled RCMs that meet predefined performance criteria so as to be considered for further high-resolution dynamical downscaling”. On line 235: “the second objective of this study, which is to evaluate the ability of the CMIP6 CORDEX-SEA ensemble in simulating climatology of four model core variables”. And later on in line 695: “the objective here is to select suitable RCMs for km-scale downscaling”. Please make these objectives explicit in the introduction (e.g. the penultimate paragraph).
2. I’m not familiar with dendrograms (Figure 14) and would appreciate a little more description on how to read this figure. What does it tell us? I can see that CCAM models are yellow and RegCM models are green, and so are similar (which makes sense). However, I’m not seeing what the manuscript says, which is that there are three “clusters” in Figs 14a,c in contrast to two “branches” in Figs 14b. I see two branches in each panel (yellow and green). Moreover, I’m not sure how this figure informs us about which models to select for further dynamical downscaling. Unless its utility to this task can be clarified, I recommend dropping this figure.
3. I think some more discussion on the potential causes of the models’ inabilities is warranted. A principal finding is that downscaled simulations substantially overestimate precipitation over the maritime continent, especially during the monsoon season. I think it’s worth adding some hypothesising to why the models have large wet cold biases. My first thought on the wet bias was perhaps the models are not adequately simulating the temperature gradient between the continent and the ocean. However another principal finding is that the simulations tended to underestimate near-surface temperature (particularly over the mainland). Perhaps the models do not represent regional SST patterns and magnitudes. I think this intrigue deserves some discussion.

Minor Comments

1. Line 28. General Circulation Model.
2. Line 51. Missing word between in “accurately mesoscale” ?
3. Line 54. There is a mix of British English (e.g., modelling, standardise) and American English spellings (e.g. standardize, urbanization, parameterization). The choice is unimportant, of course, but I wanted to point it out if the authors wish to consistently adopt one over the other.
4. Line 70. Resolution and grid spacing are not technically the same thing. Recommend replacing “at 25-km resolution” with “with a grid spacing of 25 km”.
5. Line 74–75. This sentence is nonsensical as it compares a multi-model ensemble with itself: The multi-model ensemble provides an improved representation of climatological precipitation but also exhibits larger inter-model variability compared with multi-model ensemble.
6. Line 84. “the observed and present-day climate” are the same thing. Better to say “reproducing the historical climate”.
7. Line 90. The previous sentence specifies what the authors will present. The first sense on line 90, however, I think is a general statement. When it says “Cross-ensemble evaluations of model ensembles over SEA have been conducted using various approaches” I think this is a statement about what’s in the literature, not what the authors have done. Potential confusion could be avoided here by inserting “using various approaches in the literature …”.
8. Line 90–91. The sentence “The most common…specific applications” seems like it’s missing a word or was maybe meant to be tied to the previous or next sentence?
9. Line 95. I think this is the first use of “RegCM4” but has not been defined.
10. Line 129. I think this sentence flows better when deleting the “and” before “spanning”.
11. Line 253. Since the area score metric (ASM) is not a traditional metric and won’t be familiar to readers, I think more details are warranted (e.g. how it is calculated, what a perfect score would be, and appropriate equations). Since ASM is the area between the model distribution and the observed one, is it always positive i.e. can it differentiate between model underestimation and overestimation?
12. Line 275. Since one aim of the article is to identify models for further downscaling I think this sentence would work better if it defines the models for inclusion, rather than exclusion.
13. Line 297. Should this be “It’s worth noting”?
14. Line 298. “also exhibits a cold biases”.
15. Line 299. “exhibits”.
16. Line 308. Is this a two-sided test on the observed and model mean bias errors (MBE)?
17. Line 351. “ASM” undefined.
18. Line 379. “performance over the mainland is somewhat improved compared with that in the Mainland.” Wrong word?
19. Line 392–394. “Despite this, several simulations (e.g., CCAM-Mod2021-ACCESS-CM2, CCAM-2017-ACCESS393 CM2, CCAM-2017-EC-Earth3-Veg, and HadGEM3-RA7-EC-Earth3-Veg) show relatively good agreement with observations 394 across the full distribution and across multiple reference datasets (Figs. S8-10).” This seems like an important point when identifying simulations for further downscaling. Maybe make sure this is highlighted in the conclusions.
20. Line 404. Should this sentence end “tas and tasmin” rather than “tas and tasmax”?
21. Line 406–408. I think it could be beneficial to mention here which models did actually do a good job at capturing the spatial temperature distribution.
22. Line 420. “the bias- and variance-corrected SST forcing”. Does HadGEM3-RA7  also use bias- and variance-corrected SST like mentioned for CSIRO-CCAM mentioned on line 174? If so, please note it in the methods section.
23. Lines 423–433. When describing model performance please cite the appropriate figure panels.
24. Line 427–428. Why is the GCM nudging technique mentioned when it’s the same for both models and CCAM-2017 improved over CCAM-2021? Wouldn’t it be better to cite differences in the model configurations?
25. Line 430. “The pattern of the NDJFMA reveals…” Missing word?
26. Line 430–431. “RCMs show better skill in capturing the spatial distribution of precipitation, with dry biases over the mainland and wet biases over MC.” The first time I read this sentence it sounded perfectly straightforward, but on a second reading I’m a little confused. What is being compared to when you say “better”? It doesn’t seem to follow that the models capture the spatial distribution of precipitation better than they do that of temperature because Scors are substantially higher in Figs 3 and 4’s annotations than in Figs 6 and 7’s. Please clarify.
27. Lines 446–448. “The RCMs generally reproduce the peak rainfall over the Mainland; however, most RegCM simulations fail to adequately capture the precipitation peaks over the MC.” Is this true? Looking at Fig S12, there is indeed a large wet bias like over the Mainland, but most models seem to dip during June–October. Also, rather than “peaks” isn’t there only a single peak over the maritime continent (Nov–April) that only appears as two because of how it’s plotted (with Jan and Dec on opposite ends)?
28. Lines 478–480. Initialisms help us say things more efficiently, but there are nine in this sentence. I think it’s easier to understand when writing out MSM and BMF.
29. Line 492. Annual average of daily mean near-surface temperature?
30. Line 497. “testing” or further dynamical downscaling for CARE for SEA cities?
31. Line 500. This sentence is a little confusing. What is the “strategy” mentioned here to identify thresholds? They simply seem chosen. I think the manuscript can be clearer about why these thresholds were chosen.
32. Line 503. “for [the] spatial correlation metric”?
33. Line 506. Floating &?
34. Line 509–510. In what sense are gauge stations drier? Relative to what? Is this sentence trying to say that gauge stations in regional datasets tend to be from climatologically dry regions? Please clarify.
35. Line 511–512. Delete the period. I’m confused by the word of “exceeding thresholds for at least three reference datasets. – regardless of the reference used”. (presumably the period also wasn’t meant to be there).
36. Line 519–520. This sentence is a bit confusing to me. I can see that RegCM-CESM2 and RegCM-MIROC6 are below the critical thresholds across almost all reference datasets. But Fig. 10 condenses the spatial correlation into a single number. So when the manuscript says the models fail to capture the “spatial contrast”, I feel like that references Figures 6 and 7, and I’m not sure what’s  being highlighted. Please clarify.
37. Line 536. Orange cells are very hard to see and too similar to the other colors in the colorbar. Recommend boxing out simulations that don’t meet the benchmarking expectations, as in Figure 10.
38. Line 559. Please add how confidence intervals are calculated to the caption.
39. Line 587. Please add the meaning of blue highlighted numbers to the caption.
40. Lines 627–629. Do high range, mid range and low range correspond to emission scenarios? Please clarify.
41. Line 639. Period instead of a comma?
42. Line 649. I think this sentence sounds can be simplified to give readers an easier time understanding it. By “spatial contrast” and “temporal mean precipitation distribution” do we mean ‘spatial distribution’ and ‘seasonality’? The first sentence of (2) is very clear and a good template.
43. Line 650. “tends to substantially overestimate observed precipitation intensity”.
44. Line 654. What is a “process-based metric”? Please clarify.
45. Line 658. There’s a space in CRUTS.
46. Line 681.
47. Models with 25 km grid spacing will assuredly have difficulty predicting rainfall over mountain terrain. But how does the complex terrain result in an inability to capture the spatial variability? At 25 km, don't the models have sufficient resolution to show where the topography actually is (as we see in the wet biases)? Can you clarify? This makes me wonder again about the ability of the models (GCMs and RCMs) to represent regional SST.
48. Line 708. Replace “a range of climate variables” with “temperature and precipitation”.
49. Line 712. What case study? A case study is not mentioned anywhere else in the manuscript.