The reviewed manuscript discusses the XCO2 product retrieved from the DQ-1 ACDL space-borne lidar and compares it both with other space-borne XCO2 measurements and with the reference ground-based stations. The need for accurate CO2 measurements is obvious, its mean value characterizes global changes, whereas its sources and sinks tell us about carbon cycle, emissions, and transport effects. Compared to existing and past space-borne passive measurements (SCIAMACHY, GOSATs, OCOs, TanSat), the space-borne lidar has certain advantages. It uses a controlled source of probing light whereas the passive sounders depend on the incident solar radiance that is not pointed the same way throughout the globe. In particular, this changes the observability of the nighttime part of the globe that is completely inaccessible for the passive XCO2 sounders relying on reflected sunlight (OCO-2, GOSAT, TanSat). The thermal-infrared emission sounders like AIRS, IASI, or CrIS do observe CO2 emissions, but their channels are characterized by altitude-dependent contribution functions related to CO2 concentrations, whereas the differential absorption method probes the whole atmospheric column down to the ground. For this method, the absence of solar photons contaminating the signals makes the nighttime observations preferable to daytime ones. From this point of view, ACDL might serve either as a reference for other sounders or as a strong supplement to existing XCO2 data.

The manuscript introduces the problem, discusses an extensive list of comparison datasets that includes not only spaceborne passive remote sensors like OCOs or GOSATs, but also a network of ground-based Fourier-spectrometers TCCON that retrieve XCO2 from solar near-infrared absorption spectra and serve as a reference for space-borne observations.

Throughout the manuscript, the authors show different cross-comparisons between the space-borne passive, space-borne active, and ground-based observations, and conclude that the overall agreement between the compared instruments is astoundingly good, with bias less than 0.3 ppmv and RMSE less than 1.5ppmv for all instruments except GOSAT-2. The correlation coefficients between the datasets are on the order of 0.91-0.95. The manuscript is topical, it fits well the scope of the journal, is well organized and well-written.

I believe it can be published in the journal with minor revisions, which I propose for the discussion below.

Minor comments

The abstract relies heavily on acronyms (XCO2, IPDA, TCCON, and the various instrument names) without expanding any of them at first use. This is fine for a specialist audience already familiar with the history of spaceborne XCO2 observations, but makes the abstract nearly unreadable for a general reader outside this subfield. Since the abstract is often the only part of the paper read by non-specialists, could the authors, please, either expand all acronyms at first use or rewrite the abstract in plain language?

Lacking elements of the method

Lines 55-72 and/or lines 102-113: it makes sense to add some information about the optical path of ACDL to explain why the vertically resolved lidar signal does not carry the information about the CO2 and only the column value is retrievable. The references given in this section explain this, but adding a couple of sentences regarding the method will simplify the flow.

Neither of these paragraphs discusses the differences of daytime and nighttime observations. Since the online and offline channels share nearly the same additive solar background but different signal levels, an imperfectly subtracted background biases the online/offline ratio toward unity and, therefore, biases XCO2 low – an effect that should vanish at night. Could the authors show a daytime-vs-nighttime comparison over similar scenes to bound this residual bias, if not already addressed in the cited references? Maybe it makes sense to provide a theoretical estimate for exactly the same atmospheric column to exclude any effects caused by diurnal change.

A related point: the differential absorption method requires a valid hard target (surface or opaque cloud top) to close the two-way path. Could the authors please note the fraction of shots lost to a missing hard target and whether this loss is geographically or seasonally structured?

The structure and the order of presentation

The validation with TCCON observation appears only after the diagnosis of residuals in passive spaceborne instrument comparison, whereas I’d say that TCCON observations should have more weight and therefore precede them.  See the other comment regarding the order of presentation and TCCON measurements below.

Section 3.1 is dedicated to sampling and takes a lot of space – I am not sure that it belongs to the Results. In principle, it could have been reduced or moved to Appendix without losing the main message of the section.

Throughout the Results section, some of the differences between the datasets are explained whereas the other ones are just reported. Maybe it makes sense to give the priorities or weights to these differences: for example, one chooses TCCON as a reference as in Section 3.7 and discusses the differences only with respect to these observations. Again, this will require a slight reorganization of the order of sections. If the authors believe that TCCON comparisons are of lesser importance, I’d like to understand, why – for me, they are a good candidate for the reference ones because they depend on smaller number of unknowns. 

For the figures showing geographical distribution of XCO2 differences, did the authors try to subset the datasets over the oceans using the surface winds values? Does the smoother sea produce better results?

Technical corrections

Fig. 1: the panels share the same color bar and OX/OY axes. In this case it would be logical to merge them and keep only 2xOX axis labels, 3xOY axis labels, and 1 color bar.

This manuscript showed the validation results of XCO2 from the DQ-1 satellite instrument, compared with other satellite XCO2 and reanalysis data. All analyses look sincerely conducted, results are well organized, and writing quality seems fine. However, there are some questions and comments about suggested results as below. Final decision for this manuscript will be executed after checking authors' response and revised manuscript. 

1) Readers often read abstract first, sometimes only to see the main lesson of that paper. Thus the completeness of abstract is very important. In that context, the abstract of this manuscript should be improved more. What is DQ-1 ACDL? How does it relate to the IPDA? Why do we need to evaluate the DQ-1 ACDL data? Overall, the research motivation was not well transferred from this abstract.

2) In Figure 1, the number of available GOSAT-2 data number looks larger than that of GOSAT-1 data, which is OK because the GOSAT-2 is recent one. Then why the data number of OCO-3 is smaller than that of OCO-2? Additionally, the number of available OCO-3 data  decreases over the tropical Pacific, but increases over the 40-50 °N in the North America and Asia. How to explain this? I did not see meaningful authors' statements about this issue. This seems important in terms of sampling bias factor related to provided validation results.

3) Overall, GOSAT-2 XCO2 overestimation looks obvious. What is the reason? I know that the main topic of this manuscript is DQ-1. Once the GOSAT-2 overestimation issue was suggested in this manuscript, however, authors need to explain its reason. I only found a single statement about this issue in Line 397-398 as next "This systematic positive bias may be related to the fact that the GOSAT-2 SWIR L2 product used here has not been empirically bias corrected", however, I could not figure out the meaning of this statement. 

4) In Figure 5, the bias looks large over the land of Australia and tropical Pacific. What is the reason to explain this? There was authors' statement in Line 425 "This pattern suggests that the residual differences are more regional and sampling dependent than globally systematic". Again, I could not figure out the meaning of this statement. This statement is consistent with results and discussions in next paragraphs?

5) Figure 6 (XCO2 comparison between DQ-1 and OCO-2) looks similar Figure 7 (XCO2 comparison between DQ-1 and OCO-2). In tropical Pacific, the validation data number in OCO-3 (Fig. 7) is smaller than those in OCO-2 (Fig. 6), probably due to the data number difference (Fig. 1). Based on Figure 2, I can notice that the data number difference between OCO-2 and OCO-3 suddenly becomes larger after August 2023 for a year. What happen in this period? This malfunction of OCO-3 can make the data difference in tropical Pacific between OCO-2 and OCO-3? (Over Australia, RMSE data points look similar between OCO-2 and OCO-3). Is there any special issue of OCO-3 for only ocean surface monitoring?

6) In Line 447, there is a statement as next "These patterns suggest that the spatial characteristics of DQ-1–OCO-2/OCO-3 bias and RMSE cannot be explained by surface reflectance alone, and may also be related to ocean glint observation conditions and the distribution of valid collocated samples". How the ocean glint differs? Is there any clue that can be applied to high RMSE in tropical Pacific ocean? Since the bias is especially large over this tropical Pacific (different from other ocean area), authors need to add more detailed statement about this part, at least based on previous study results.

7) Please cite figure number appropriately after important statements (especially in chapter 3.4). I cannot make good connection between written statements and suggested figures. 

8) Figure 8 (DQ-1 vs. CAMS-derived DQ-1) is interesting. The RMSE is also high over Australia, Sahara desert, and tropical Pacific, similar to FIgure 6 and 7. Then my question is how about the comparison results between OCO and CAMS-derived XCO2? If they are closer, then DQ-1 research team can achieve some tips about bias factors.

9) Please add the site name in the caption of Figure 10 (there are many abbreviation in the legend, but I do now know what those are). 

General comments:

This manuscript presents an intercomparison of DQ-1/ACDL XCO₂ with OCO-2, OCO-3, GOSAT, and GOSAT-2. The topic is relevant, and the comparison of active and passive XCO₂ observations is useful. My main concern is that several choices in sample selection and collocation are not sufficiently justified, which makes it difficult to assess the robustness of the reported agreement. I believe the paper makes a valuable contribution and is suitable for publication after revision.

Special comments:

• The analysis is restricted to the top 10% of grid cells based on the coherence and sampling score. Why was the 90th-percentile threshold chosen? Because this selection favors relatively homogeneous, well-sampled regions, it would be useful to show whether the reported bias and RMSE change when a less restrictive threshold is used.
• The comparison uses 2° × 2° daily averages with a maximum time difference of 4 h. Please clarify the rationale for these choices, especially the 4 h threshold. A simple test using a smaller spatial or temporal window would help show how sensitive the results are to the collocation criteria.
• CAMS is involved in the region screening, column-definition correction, and nighttime DQ-1 comparison. How large is the column-definition correction in practice? Showing the comparison statistics before and after this correction would make its influence on the results clearer.
• GOSAT-2 shows a large positive bias relative to the other products, but the manuscript also notes that this product has not been empirically bias corrected. This makes the direct comparison with bias-corrected OCO products somewhat difficult to interpret. The discussion should clarify this distinction, or include bias-corrected GOSAT-2 results if available.
• All satellite products are further restricted to 380–480 ppm after the product-specific quality screening. What is the reason for applying this additional range filter? Please also report the number or fraction of observations removed for each product.
• The TCCON comparison provides an important independent evaluation, but more information on the collocations is needed. Please report the sample size for each satellite and indicate, if possible, whether a small number of stations dominates the pooled statistics. Some estimate of uncertainty for the reported bias and RMSE would also be useful.
• Please clarify whether the daily grid-cell XCO₂ values are simple arithmetic means or uncertainty-weighted means.
• The reduced OCO-3 coverage during the stowage/reconfiguration period should be separated from limitations caused by clouds, aerosols, or solar illumination.
• The title “First comparison” seems somewhat strong, since previous studies cited in the manuscript have already compared or validated DQ-1 against other XCO₂ datasets. The main novelty appears to be the unified multi-satellite comparison framework.
• Please check the manuscript for typographical errors.