This paper is a noteworthy addition to the growing literature of applying ML to Earth Science remote sensing, in this case cloud remote sensing. As the observational systems change and certain capabilities are interrupted or discontinue, we need innovative methods to cover data gaps. AI and ML have a lot to offer in that regard.

My recommendation is to accept the paper with minor revisions. I was tempted to recommend a major revision because I think that the active dataset is not used to its full potential, but it'd be unfair to demand from the authors a paper of a different direction. So this paper is judged on the merits of the fundamental choices they have made. Still, there are some major issues on how ML was implemented (see below).

What makes this paper in my opinion less than it could have been is the information content of the active observations they decided to use: so-called "cloud types". There are two issues: (1) The main appeal of active observations is that they can resolve (in many cases) cloud vertical structure, so to choose just a cloud type "flag" (the only or most dominant cloud type in an active observation "ray" or profile) seems like the least interesting choice; (2) Even these "cloud types" from 2B-CLDCLASS-LIDAR (CC-L) are taken too literally by the authors: they're not the WMO cloud types the authors imply them to be, but just cloud labels that may have some association; but I suspect frequently they do not have the morphological characteristics that surface observers use to classify clouds. More on this topic below.

On cloud types:

The WMO has 10 main cloud classifications https://cloudatlas.wmo.int/en/cloud-classification-summary.html. There's no Deep Convection (DC) as in CC-L, but rather Cumulonimbus (Cb). There is also cirrocumulus and cirrostratus, which do not exist in 2BCL (presumably all under Cirrus). But the biggest tell of the lack of correspondence between CC-L and WMO is the small occurrence frequency of stratus (noted by the authors). If one checks daytime stratus occurrence over the ocean from surface (ship) observers in the Warren Atlas https://atmos.uw.edu/CloudMap/WebO/index.html, there is plentiful stratus in the extratropical oceans. So, the same way the 9 ISCCP cloud types defined by arbitrary boundaries in the TAU-CTP joint histogram cannot be taken as equivalent to the corresponding WMO cloud types (cloud morphology as appears from surface), mentioned by the authors, the CC-L cloud types can also be assumed to mean the same thing as the WMO classification. Another tell: the mall SW CRE peak the authors find for Ns which are optically thick rain-producing clouds.

Other major comments:

-- Applying the ML algorithm cloud type to off-nadir MODIS pixels, when the training has been conducted with nadir MODIS cloud retrievals (that coincide with the active observations) can be justified only if it has been previously shown that the cloud retrievals are statistically the same at different parts of the MODIS swath, i.e., there are no biases in off-nadir retrievals (especially as one moves further aways from nadir).

-- Similarly, applying the ML algorithm to ESACCI clouds (from AVHRR) has to be justified by showing that MODIS and AVHRR cloud property retrievals are statistically equivalent (the authors are aware they're not -- lines 337-338). Because of different retrieval algorithms I doubt they are. Also, does ESACCI also include morning clouds? The training was conducted with afternoon clouds.

-- Similarly, applying the ML algorithm on climate models clouds is a huge stretch. What makes the model clouds equivalent to those of MODIS? You cannot even rigorously define a cloud-only grid column optical thickness when the grid is not overcast (depends on cloud fraction profile and overlap). At the minimum the model should've provided cloud output from the MODIS simulator.

Some minor comments:

-- Figure 1 would have been more complete if the datasets used in each step (CUMULO, ESACCI, etc) were added as labels.

-- Figure 2: Unclear to me: So if I properly weighted land and ocean and then normalized by the undetermined, I'd get close to what is shown in Fig. 2a? Perhaps you can say that.

-- Figure 4: Why do this over the southern oceans where (beyond the nearly non-existent St) there is virtually no Cu and no DC? Perhaps show time series and trends for different regions for each cloud type, namely the areas where each dominates relative to its global mean? (Fig. 6)

-- Figure 7: An interesting complement to Fig. 7 would be a table showing the contribution of each cloud type to the global CRE, weighted by RFO (subject to the disclaimer of no "pure" grid cells).

-- Lines 319-322: Of course there'll be few St in CCClim since you started with small frequency of St in CC-L. Same with DC. The difference between DC and St is though that DC are truly very rare while St are artificially rare in CC-L.

-- Lines 344-346: Not sure what you mean by this.

Please find my review in the attached document.