General comments:

This manuscript presents the evaluation of the operational chemical weather forecasting system of ECCC, which is described in the Part 1 paper. These continuous presentations will be useful to understand the current modeling performances. The authors conducted a comprehensive analysis of gas, aerosol, and depositions, and discussed them well. As a critical concern, I am confusing the wording “RAQDPS023” throughout the manuscript. It will be better to clearly define “RAQDPS-FW023” and “RAQDPS-OP023” in Abstract (lines 22 and 24) and in Introduction, and then throughout the manuscript. Maybe, the wording “RAQDPS023” in line 22 would be “RAQDPS-FW023”. Although the wording “RAQDPS-OP023” was not introduced, I can see this expression in Table 7, and I feel these clear expressions will be needed to clearly follow the context of this manuscript. In addition to this clarification, I have several specific and technical comments as follows.

Specific comments:

• Lines 1271-1287 and Table 7: From this part, I can see that there was a comparison in operational and forecast for 2021/22; however, we cannot see such comparisons in the following section 4.4 (Fig. 19 and other figures in the supplemental file). Excuse me if I missed this comparison approach, but could you clarify this point?
• Line 1404-1457: It would be much better to explicitly mention the reason for the worsened R values in the forecast case.

Technical points:

• Please confirm a subscript for the chemical species throughout the manuscript.
• I am wondering about the usage of the supplemental file within this journal or the outer storage (zenodo repository). Could you confirm with the editorial office?
• Line 1828: Typo “PM3.5”.

The RAQDPS is one of the main North-American chemical weather / air quality forecasting systems. A detailed documentation of both the modelling aspects as well as the scores from the comparison with observations at the surface are valuable for the large group of users of the forecasts, and is provided by the part 1 and 2 of this article. The paper part 2 by Moran et al. is providing a very comprehensive evaluation of the skill of the modelling system to predict the main air pollutants ozone, NO2, PM2.5, relevant for health. Apart from this the authors compare to available other trace gas observations as well as measurements of the chemical composition of the aerosols in air and in precipitation. This extra set of comparisons give important additional information on the skill of the model concerning the emissions and chemical components that contribute to the formation of the main pollutants.  The scores are compared to benchmarks for chemistry modelling and to other AQ prediction efforts, which puts the scores in perspective.  In particular section 4.4 is a highlight of the paper because it identifies processes that may be responsible for differences with the observations, and discusses potential improvements in the model. The authors are honest in documenting the comparisons, showing skill for ozone and NO2, but more mixed results for PM2.5 (Fig. 8, 11, 15).

The paper is a very complete reference to the skill of the RAQDPS model to simulate surface concentrations. It is well written, and has a good list of important references.  At the same time it is also very long, and even interested / motivated readers may be hesitant to go through all the results. Nevertheless, the paper, together with part 1, serves as a complete reference for RAQDPS. As such I am in favour of publication after the authors have answered my more minor comments and questions.

Comments and questions:

l 63: "expanded rapidly over the last two decades". I found that the discussion of air quality forecasting systems worldwide, as provided in the introduction, was providing a valuable historic overview of relevant literature but the references are not fully up-to-date and may be expanded to other countries/continents (e.g. China, Japan, Europe, global-scale forecasting). For instance, in Europe the regional air quality forecasting is organised by the Copernicus Atmosphere Monitoring Service (CAMS) and is described in a recent paper by Colette et al, https://doi.org/10.5194/gmd-18-6835-2025, which I propose to add to the references.  

l 145: "dynamic; and probabilistic" evaluations are not discussed in this paragraph.

l 305: "Note also that neither system version considered some other types of natural emissions, .."

This may be formulated more clearly. 

l 356: "Two automated data filters .." Stations close to major roads or other big emitters will not be representative for a model grid cell of 10km diameter.  Is a site classification available for the stations in the US and Canada? Did the authors consider to remove stations that will not give representative comparison results? (Urban vs rural statistics is discussed in line 475)

Sec 2.3: As far as I understand, AirNow is employing NO2 analysers which use molybdenum converters. The resulting NO2 measured values are known to overestimate NO2, because the technique is also sensitive to other NOy components which are also converted in the instrument. Please discuss this aspect and how it influences the NO2 evaluation.

Fig.1. Looking at the figure it seems that the model values at the stations does not match the continental modelled distribution, especially for NO2 and PM where model-at-station values seem to be higher than the background? This figure was confusing to me. 

l 637: "NME scores" Should this be NMAE? Please use consistent acronyms.

l 656: "U.S. NOx emissions used for these forecasts may have been too low ". Are there other indications, e.g. from literature, that this is the case?

l 776: "The fact that annual NMB scores for gravimetric PM2.5 mass measurements are more positive than those for continuous PM2.5 measurements is thus surprising."

After reading the paper these differences and comparisons remain unclear to me. Maybe a few sentences in the concluding section could be added to highlight this aspect once more, and how this impacts the interpretation of the evaluation.

l 838: "but these scores were confounded by the model’s inclusion of isoprene oxidation products in this lumped VOC species (Moran et al., 2025), suggesting that overpredictions should be expected." Is there an estimate of the ratio of pure isoprene to lumped "isoprene", e.g. is a factor of more than 3 for the model/obs ratio to be expected? 

l 1226, Fig 19: This figure is a key result to document modelling improvements over time, showing improvements in O3 and NO2 scores, but not for PM2.5. l1234: "The 2010‒2019 seasonal R scores for PM2.5, on the other hand, are lower than those for NO2 and O3, are less cyclical seasonally, and do not show any improvement with time. l 1256, wildfire: Important to improve the scores, especially PM2.5 in Summer. Is it considered to use RAQDPS-FW023 to replace RAQDPS023 as operational system?

l 1442: Could the boundary conditions for ozone be partly responsible for ozone biases and east-west gradients? Please discuss.

l 1493: Wet deposition modelling is mentioned as cause for low NH3. What about dry deposition, which is  a dominant removal mechanism? Please discuss.

In section 4.4 there could be more attention for transport aspects, vertical mixing, stability. The evaluation  focusses on the surface but vertical profiles may have issues as well. A few remarks on this would be welcome.

Sec 4.4: Maybe good to add a remark on the injection height of emissions in this section. First layer vs. first two layers was discussed earlier.

Will  RAQDPS-FW023 with biomass burning emissions replace the RAQDPS forecasting system in the future? Please discuss pros and cons.