General comments.

The manuscript presents a construction of surface CO2 flux dataset for European domain, designed for near-realtime updates and based on recent energy statistics and weather data. The flux dataset was evaluated via comparing the transport model-simulated timeseries to atmospheric observations and comparing fluxes to an urban flux tower data and demonstrated a reasonably good performance. The manuscript only requires minor revisions and can be accepted after implementing corrections and amendments based on suggestions provided below.

Detailed comments.

It is advisable to enhance the part on validation of the SiB4 CO₂ terrestrial biospheric fluxes (in terms as midday fluxes or daytime mean, where fluxes are more curtain) against data-driven products available at similar resolution (~0.1): eg FLUXCOMM (Jung et al. 2020), Zeng et al (2020) or others, which can be reported summarily by region or dominant vegetation type. It is mentioned that Smith et al. 2020 did evaluation, but they did not have advantage of using a downscaled product which makes matching resolutions easier.

Technical corrections.

L1 ‘We present the CarbonTracker Europe High-Resolution system’. General reader familiar with CTE would suspect the CTE-HR is an inverse modelling system like CTE, but here the name CTE-HR is given to a set of [prior] fluxes, so it is better to explain the difference in the abstract, eg write something like: ‘We present the CarbonTracker Europe High-Resolution system fluxes’, and note that these fluxes are unoptimized (either in the abstract or in the text, eg lines 115-118)

L49 For Paris example (Breon et al 2015), one can cite a recent paper by Nalini et al 2022

L54 N2 -> N2O. In case of Swiss methane emissions, Henne et al 2016 is more widely cited.

L86-89 As for high resolution, operational NRT biospheric flux products one can mention SMAP L4C (Jones et al 2017)

L96 CAMS is associated with a wide variety of products related to the topic of this paper, better give more specific name like CAMS-REG. Also, the paper by Kuenen et al 2022 appears to document CAMS-REG-AP (air pollutant) inventory, while  GHG portion called CAMS-REG-GHG was set aside.

L168-184 It is not clear, what data is used for spatial emission patterns.

L215 Final revised paper for GFAS is Di Guiseppe et al. 2018 (https://doi.org/10.5194/acp-18-5359-2018)

L231 Can cite here the (Chevallier et al 2019) method as ‘poor man’s inversion’ as done by Chevallier et al. (2010, 2019)

L249 “Emissions by” can be omitted.

L255 ‘exact monthly growth rate’ may draw doubts, as the rate is not that exact, can write ‘exactly follow monthly growth rate’ instead.

L280 Need to add spatial/vertical resolution at which IFS winds are used, and STILT domain geographical boundaries.

L471 Suggest clarifying the text: “Currently, CTE-HR only provides CO2 fluxes”. Better be more specific: eg biogenic/biospheric/net ….

L524 Here, fluxes ‘will be made available ', while on L518 same fluxes ‘are available’

References

Chevallier, F., et al.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, J. Geophys. Res., 115, D21307, doi:10.1029/2010JD013887, 2010

Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A.: Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions, Atmos. Chem. Phys., 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019, 2019.

Henne, S., Brunner, D., Oney, B., Leuenberger, et al. Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling, Atmos. Chem. Phys., 16, 3683–3710, https://doi.org/10.5194/acp-16-3683-2016, 2016.

Jones, L., et al.: The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO₂ Exchange. IEEE Transactions on Geoscience and Remote Sensing. 55. 6517-6532. 10.1109/TGRS.2017.2729343. 2017.

Nalini, K., Lauvaux, T., Abdallah, C., Lian, J., Ciais, P., Utard, H., et al. (2022). High-resolution Lagrangian inverse modeling of CO2 emissions over the Paris region during the first 2020 lockdown period. Journal of Geophysical Research: Atmospheres, 127, e2021JD036032. https://doi.org/10.1029/2021JD036032

Zeng, J., et al. Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest. Sci Data 7, 313 (2020). https://doi.org/10.1038/s41597-020-00653-5

The study by van der Woude et al. (2022) described the CarbonTraker Europe High-Resolution (CT-HR) system that estimates carbon dioxide exchange over Europe at high-resolution (0.1 x0.2) in near real-time (about 2-months latency). The system includes a dynamic fossil fuel emission model, hourly net biosphere exchange (NEE), and an ocean flux. All these flux components are hourly. The high spatiotemporal resolution is realized by incorporating easily available statistics on economic activity, energy use, weather, and land cover map. The study evaluated the CT-HR in several aspects, including: 1) comparing the simulated CO2 to atmosphere CO2 observations at ICOS sites; 2) analyzing the 2018 Europe drought signal; 3) analyzing fossil fuel emission anomalies due to the COVID lockdown; and 4) comparing the hourly fluxes against observations from a flux tower at a city center. Overall, the paper is well written. The low-latency high-resolution data will facilitate the high-resolution atmospheric flux inversions, and the near-real time aspect can provide rapid information on carbon cycle changes over Europe. My main concern is the uncertainty quantification and the evaluation of the high spatiotemporal resolution aspect of the product. Please see my detailed comments below. 

• For each of the flux components, it would be critical to have uncertainty estimation in order to use them in the atmospheric flux inversions and provide useful information on carbon cycle changes, which are the two applications envisioned by the authors. To some degree, the uncertainty is as important as the absolute flux estimation itself for atmospheric flux inversion application. So I would suggest adding uncertainty estimation in the product that propagate the uncertainty of the input data to the uncertainty of the end products.
• The evaluation of the high spatiotemporal feature of the product is not sufficient. The paper underwent extensive effort to evaluate the product, but the evaluation is mainly on large spatial and monthly time scale (except the tower comparison and partially the ICOS comparison), which are actually mainly from the input dataset. For example, the 2018 drought was evaluated on an aggregated region, the COVID signal was also evaluated on the continental scale by comparing to the published results. As the high spatiotemporal feature is the main advancement, I would recommend adding more evaluation at finer spatiotemporal scales. For example, the observations from flux towers can be used to evaluate the hourly NEE from SIB4.
• It is not clear how the diurnal cycle of SIB4 was calculated, and what is the temporal frequency of SIB4 output. Is the original SIB4 output monthly or daily?
• Figure 6 shows that the HR almost has the same mean RMSE as the PRI, but the text claims that the CT-HR performs better. It would be informative to include the exact RMSE from PRI and CT-HR when comparing to ICOS network in the text.
• Figure 7 shows that all three fluxes give very similar results during well-mixed hours, but it is hard to tell which one has better performance at the bottom two panels since the scale is so large. I would suggest adding panels to show the difference between the simulated run and the measurements. I would also suggest discussing RMSE of CO2 forced by these three fluxes during well-mixed hours and during night and early morning.
• It is strange that the “Flat” fluxes seem to have the same performance as the “full res” in December, which seem to indicate potential large errors in boundary layer height. I would suggest evaluating boundary layer height information used in STILT and isolate the impact of transport and fluxes on the flux errors shown in Figure 7.