511 Title : Evaluation of various daily precipitation products for large-scale hydro-climatic applications over Canada

Paper Summary This paper sought to evaluate the performance and reliability of daily gridded precipitation products for Canada – based on seasonality and eco/hydro-zones. The aim of defining specific climatic/hydrological regions and factoring in seasonality was to relay more usability and relatability with the results. The authors identified a need for such study as few had been done previously which looked at precipitation products for Canada – although they do make reference to a study being conducted previously for “North America”.

The abstract seems too long and needs to be further condensed in the revision.Moreover, the spatiotemporal scales of evaluation (daily and 0.5 deg.) should be denoted in the abstract.
The length of the abstract will be reduced and the spatiotemporal scales of evaluation will be included in the revised abstract.The following shows the revised abstract, with deleted materials being crossed out by drawing a line through them (and revised sentences being coloured in red): A number of global and regional gridded climate products based on multiple data sources and models are available that can potentially provide better and more reliable estimates of precipitation for climate and hydrological studies.However, research into the reliability of these products for various regions has been limited and in many cases non-existent.This study identifies several gridded precipitation products and over Canada and develops a systematic analysis framework to assess the characteristics of errors associated with the different datasets, using the best available adjusted precipitation-gauge data as a benchmark over the period 1979 The impact of snow cover on passive microwave sensors will be addressed and the GPM mission will be mentioned in the revised manuscript.Accordingly, the corresponding references will also be added.The following shows the revised discussion of satellitebased estimates in the original manuscript [P4:L7-14], with additional sentences being coloured in red: Development of satellite-based precipitation estimates has provided coverage over vast gauged/ungauged regions with continuous observations regardless of time of day, terrain, and weather condition of the ground (Gebregiorgis and Hossain, 2015).The recently launched Global Precipitation Measurement (GPM) Core Observatory has further opened up new opportunities for observing worldwide precipitation from space (Hou et al., 2014).However, satellite-based estimates also contain inaccuracies resulting primarily from temporal sampling errors due to infrequent satellite visits to a particular location, instrumental errors due to calibration and measurement noise, and algorithm errors related to approximations to the cloud physics used (Nijssen and Lettenmaier, 2004;Gebremichael et al., 2005).In particular, the passive microwave overpasses were shown to be unreliable over regions with snow cover and complex terrain such as the Tibetan Plateau (Yong et al., 2015).

3.
P17 Line 10-14: Using the approach of Kolmogorov-Smirnov test to evaluate different precipitation products is an interesting way for readers.But here the equation ( 1) is not clear.I suggest that the authors may carefully re-modified the calculating equation and illustrate the meanings of parameters.If possible, an appendix that introduces the Kolmogorov-Smirnov test might be added at the end of the text.At least, the Eq. ( 1) should be revised again.
We will address this comment by providing better explanation of the calculation and revising the wordings in the equation for better clarity in the revised manuscript.We think that this statement in the Conclusion [P27:L12-14] of the original manuscript will cause some confusions and we decide to drop it from the conclusion and address the reasons of the poor performance in the Results Section (Section 5.2) [P22:L23] in the revised manuscript, which is shown as follows: The resulting values of the RMSE metric in Regions 7 (Atlantic Maritime) and 13 (Pacific Maritime) tended to be larger than that of other areas.However, the other metrics such as correlation coefficient and PBias showed better performance in these regions.This suggests that higher RMSE values can be mainly attributed to the fact that precipitation amounts are higher in the maritime regions.

5.
Some figures are not very clear and they should be modified or redrawn.For example, there is no whole Canada map (or North American map), no north arrow, no measuring scale in Fig. 1. Figure 2 is OK, but the plots in Fig. 3 and Fig. 4 are too small and not clear for reading.I really hope that these plots could be better displayed in the revised manuscript.
We agree that some of the figures are not very clear as it is also commented by Reviewer 1.We will enlarge the figures as much as possible and provide the missing map information in Figure 1 in the revised manuscript.In response to comment 3 of Reviewer 1, we decide to limit the evaluation period to 2005 instead of 2012 for the climate model products.Accordingly, Figures 2, 3, and 4 in the original manuscript will be reproduced to reflect the change.In short, the evaluation for the climate model products from the period of 1979 to 2005 will be shown separately from that of station-based and reanalysis-based products.Thus, Figures 3 and 4

will only show the distributions of pvalue of the K-S test for the station-based and reanalysis-based products and a new
Figure 5 will be created to show the distributions of p-value of the K-S test for climate model products in the revised manuscript.The numbering of Figures 5 to 8 will also be changed accordingly.Note that all the figures in the supplementary materials will also be subject to the same changes as aforementioned but will not be shown here.The revised figures are shown as follows:      4).Each hollow circle represents one p-value of the K-S test conducted at one precipitation-gauge station, with no stations in Region 1 (R1).The p-values of Regions 6 to 9, and 13 to 14 (R6-R9, and R13-R14), which have more than or equal to 10 stations, were shown in box-whisker plots with bottom, band (black thick line) and top of the box indicating the 25 th , 50 th (median), and 75 th percentiles, respectively.
the conclusion, please clarify and explain the reasons of the poorest performance of station-based and reanalysis-based products in Atlantic and Pacific regions.

Figure 1 .
Figure 1. 15 terrestrial ecozones of Canada with numerical codes indicating Region from 1 Arctic Cordillera to 15 Hudson Plain.Big (a total of 145) and small (a total of 137) white dots are the extracted precipitation-gauge stations from the Canadian adjusted and homogenized precipitation datasets of Mekis and Vincent (2011) for the period of 1979 to 2012 and 2002 to 2012 respectively.Black dots are major cities in Canada.

Figure 2 .
Figure 2. The percentage of reliability, calculated by the Eq.(1), of each precipitation dataset in four seasons for the period of 1979 to 2012 (left panel) and 2002 to 2012 (right panel) across Canada.The higher the percentage, the more reliable the precipitation dataset.Different colours represent different precipitation products, with magenta representing the whole PCIC datasets and cyan representing the whole NA-CORDEX datasets.The full names of the precipitation products are provided in Tables 1, 2, and 3.

Figure 3 .
Figure 3. Distributions of p-value of the K-S test in the 15 ecozones in four seasons for the period of 1979 to 2012 (long-term comparison without CaPA).Note that the numbers of precipitation-gauge stations in each ecozone are different (see Table4).Each hollow circle represents one p-value of the K-S test conducted at one precipitation-gauge station, with no stations in Region 1 (R1).The p-values of Regions 6 to 9, and 13 to 14 (R6-R9, and R13-R14), which have more than or equal to 10 stations, were shown in box-whisker plots with bottom, band (black thick line) and top of the box indicating the 25 th , 50 th (median), and 75 th percentiles, respectively.

Figure 4 .
Figure 4. Distributions of p-value of the K-S test in the 15 ecozones in four seasons for the period of 2002 to 2012 (short-term comparison with the inclusion of CaPA).Note that the numbers of precipitation-gauge stations in each ecozone are different (see Table4).Each hollow circle represents one p-value of the K-S test conducted at one precipitation-gauge station.The percentage of missing values in precipitation-gauge station in Region 11 (R11) exceeded 10% and thus no K-S test was conducted.The pvalues of Regions 6, 8 to 9, and 13 to 14 (R6, R8-R9, and R13-R14), which have more than or equal to 10 stations, were shown in box-whisker plots with bottom, band (black thick line) and top of the box indicating the 25 th , 50 th (median), and 75 th percentiles, respectively.

Figure 5 .
Figure 5. Distributions of p-value of the K-S test in the 15 ecozones in four seasons for the period of 1979 to 2005 (long-term comparison of PCIC and NA-CORDEX).Note that the numbers of precipitation-gauge stations in each ecozone are different (see Table4).Each hollow circle represents one p-value of the K-S test conducted at one precipitation-gauge station, with no stations in Region 1 (R1).The p-values of Regions 6 to 9, and 13 to 14 (R6-R9, and R13-R14), which have more than or equal to 10 stations, were shown in box-whisker plots with bottom, band (black thick line) and top of the box indicating the 25 th , 50 th (median), and 75 th percentiles, respectively.

Line 10-14: In terms of retrieval errors in satellite precipitation, the impact of the snow cover on passive microwave sensors is rather serious over high mountainous regions or high latitude areas, e.g. the Tibetan Plateau (Yong et al., 2015). The authors should address this issue here. Additionally, the Global Precipitation Measurement (GPM; Hou et al. 2014) has been coming and the authors should mention the GPM mission in describing the satellite precipitation estimates. Hou, A. Y., and Coauthors, 2014: The global precipitation measurement mission. Bull. Amer. Meteor. Soc., 95, 701- 722. Yong, B., and Coauthors, 2015: Global view of real-time TRMM multisatellite precipitation analysis: Implications for its successor global precipitation measurement mission. Bull. Amer. Meteor. Soc., 96, 283-296.
to 2012.The framework quantifies the spatial and temporal variability of the errors over 15 terrestrial ecozones in Canada for different seasons over the period 1979 to 2012 at 0.5 ° and daily spatiotemporal resolution at the daily time scale.Results showed that most of the products were relatively skillful in central Canada.