General comments

This paper describes a standardized benchmarking framework for selecting CMIP6 GCMs for CORDEX downscaling over Southeast Asia. The topic is important because Southeast Asia faces a high risk of flooding due to climate change, yet fewer models or frameworks are available for characterizing regional changes in precipitation compared to other regions such as Europe and the US. The authors did a great job highlighting the differences between their approach and those in the literature, which mainly rank GCMs according to specific evaluation matrices. The logic of this paper is very clear, and it is very well written. I only have a few minor points for the authors to consider.

Technical corrections

L44: GCMs’

L51, L55: should be ‘WCRP’?

Section 1: an overview of the paper structure should be added to the end of this section so the readers know what they expect in each section.

L120-121: do you mean ‘We do not consider models which have a horizontal grid spacing greater than…’. Or by ‘greater’ do you mean finer resolution than 2 degrees?

L123-124: incomplete sentence.

L174: you may want to remove theta from the first half of the sentence and explain it as wind direction(?)

L198: did you define DMI somewhere above?

L218: what do you mean by ‘significant sign’?

L320-322: not sure if I follow the definition or description of the benchmarking threshold. Do you find the six wettest and driest modelled months and require the four wettest and driest months from observations to be within those six modelled months? Then how is the threshold determined?

L340-342: did you show the observational trend somewhere or can you cite references for this claim?

Review on

Selecting CMIP6 GCMs for CORDEX dynamical downscaling over Southeast Asia using a standardised benchmarking framework

By Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor

General comments

In this study, the authors proposed an approach to select suitable GCMs for dynamical downscaling. This approach includes a standardized benchmarking framework that consists of two steps. One is based on minimum performance requirements in terms of the reproducibility of simulated precipitation. The other is associated with the representation of simulated key precipitation drivers and teleconnections. The second step seems to be unique and reasonable. However, there are some concerns as mentioned comments written below. The most important one may be the method for determining threshold values of metrics to judge whether a model well reproduces precipitation itself, key precipitation drivers, and teleconnections.

Major comments

1. L147:

Perhaps the authors forgot to put section 2.2.1 just after this line. Putting here an explanation of fundamental metrics, such as MAPE and Scor, would be preferable.

1. L158:

Maybe a good model performance based on key physical process in the historical climate does not always guarantee a good performance in terms of future climate. This is the same situation as the case of MSMs, as the authors mentioned.

1. L244:

Maybe relative change would not always be a good indicator. Wouldn’t it be OK if the authors could also check the difference between the two (future minus historical), in particular, in a dry season?

1. L251:

Using satellite data, such as TRMM and CMORPH, enables the authors to validate simulated precipitation over ocean as well.

1. L289:

How about using RMSE as a metric to validate simulated precipitation. What do the authors think about it?

1. L313:

Do the authors think that further validation is needed by using another observational product, such as CHIRPS?

1. L315:

The method for determining threshold values seems to be important, as the authors mentioned here. Wouldn’t it better to determine the number of models that would be used for downscaling first, and then, to choose models in order of better performance? In this case, the authors do not need to determine threshold values.

Minor comments

1. L131:

It would be better to write the resolution of ERA5 here, which would be helpful for readers.

1. L170:

There seems to be no description of the abbreviation of MAPE.

1. L173:

Could you explain the advantage of this metric? How about a metric as follows:

Sqrt((Ui-Ui,ref)**2+(Vi-Vi,ref)**2)

1. L174:

Typo? Should we delete “theta i theta ref”?

1. L274:

The threshold values seem to be somewhat subjective. What made the authors deduce these values.

1. L307:

“Consequently” would not the right word here because the performance of biases does not always result in that of correlation.

1. L340:

There seems to be a decreasing trend.

1. L407:

Figures in bias seem to be preferrable for clear understanding of this discussion: overestimation of the wind intensity relative to ERA5.

1. L509:

The linear relationship is not necessarily needed because it is between the changes of temperature and precipitation, not between temperature and precipitation themselves.

1. L533, L543:

The number of clusters seem to be somewhat subjective. It would be preferrable to describe what is behind these specific numbers.

Review: “"Selecting CMIP6 GCMs for CORDEX Dynamical  Downscaling over Southeast Asia Using a Standardised Benchmarking Framework "

The manuscript proposes a hierarchy of statistical indices and precipitation features (drives, teleconnection pattern, and climate change signal)  with the aim of selecting the most suitable CMIP6 global climate models (GCMs) to be used for dynamical downscaling in Southeast Asia (SEA). The proposed methodology evaluate simulated precipitation following the steps: a) first, GCMs ability to simulate precipitation in SEA is statistically checked considering their mean absolute relative error, spatial correlation coefficient, annual cycle and time trends; b) second, the 850 hPa winds are used to discuss the ability of GCMs in reproduce observed monsoon characteristics, while the teleconnections are evaluated by considering the time correlation with two SST indices for ENSO and IOD; 3) third, GCMs are checked considering their independence and climate change signal in future SSP-3.70 scenario. Overall, the methods used are appropriate to  reach the aims of selecting GCMs, with an abstract/conclusion reflecting the main results that recommends two independent groups of GCMs to dynamical downscaling in SEA. However, I have some minor comments before the acceptance of the manuscript. 

Minor points 

1. I would like to know from the authors if all models in Table 1 have enough data  available (atmospheric variables in three dimensions at each 6 hours) for dynamical downscaling. I did not have time to do this check. 

2. Section 3.3 - I do not understand the criteria of analyzing the "... GCMs that simulated at least monthly tas (near-surface air temperature) and pr (precipitation) for the SSP-3.70 scenario only …" since simulations having only these two variables are not appropriated to dynamical downscaling. We know that atmospheric variables in three dimensions at each 6 hours  are required for dynamical downscaling.  In my opinion this should be the first criteria to select GCMSs, being essential to exclude models that do not pass this criteria of the manuscript analysis.  Please, clarify.  

3. L530-531 - Please, remove the affirmation "historical simulations were constrained by … observed SST)" since it is not correct because all GCMs listed in Table 1 are coupled GCMs having an oceanic component.  Therefore, even in the present climate the SST is a model product without any "constraint" with observation.

4. L184-202 - The description of how ENSO/IOD indices are correlated with seasonal precipitation is hard to follow. I Suggest to the authors to include a diagram to make this point clear. 

L120 - should be "grid spacing smaller than 2 x 2 "  since there is no model with grid spacing "greater than 2x 2" in Table 1. 

L123 - The phrase "At the time …" is incomplete.  What happens with the first member of some models?

L144-147 - Plese, use these lines to define BMF and MSM. They only are defined in the legend of Figure 1.

L174 - Please, write that theta is the wind direction … and only  "ui refer to simulated wind speed …"

L183 - should be " … ENSO/IOD indices"

L198  - "DMI"  is not yet defined. Move its definition in L205 to L198.

L207 - Please, move "We use a … " as a new paragraph. 

L260 - Write out  "MAPE" to be coherent with "Spatial correlation (Scor)".

L264 - Please, use the correct symbol for "greater or equal".

L281 - Please, to make clear what is "regionally-averaged climatologies". Is it referring to the average over all grid points over the continent inside the domain?

L337 - should be "... the signal of statistically significant … trends using the wet (Fig. 5) and dry (Fig. 6) seasons accumulated precipitation "

L366 - remove "of variable sign"

L369 - should be "temperature annual cycle"

L403 - Please, to improve the description of the winds.  I am seeing "easterly-northeasterly winds in the North Hemisphere" crossing the Philippines. 

L405 -  should be "westerly winds predominate between …"

L410  - should be " … all MSM-selected models for precipitation …"

L417 - should be " … the extended summer season …"

L414-415 - change to "To benchmark CMIP6 GCMs, three metrics (HR, MR and FAR, see section 2.2.3) are calculated for each GCM considering the thresholds ≥50% for HR and ≤65% for MR and FAR, given the limited number of simulations used  at this stage".

L433 - to refer to Fig. 10 " …CMIP6 GCMs (Fig. 10)"”

L463 - should be "... metrics stages (Fig. 11)."

L509 -  change " … both signal and magnitude …"

L533 - remove the last "MJJASO"

Figures

Figures 4,5 - It is hard to see the wind direction. Please, improve these Figures, maybe using a less intense shading for wind speed.   

Figures 2,3 - the scale in the bottom is in %, but the mean values in the boxes synthesizing MAPE in SEA  are in hundredths. I would like to ask the authors to use only one unity for the same variable, for example, changing the values inside the box to %. A  similar  problem occurs in Figures  7 and 8, L295-296. 

Figure s2 - should be "The annual climatological (1960-2014) bias of temperature …"