the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Apr 2026
The Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset: A 45-year high-resolution analysis for North America (1980–2024)
Abstract. The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(17697 KB) - Metadata XML
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Supplement
(11548 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-620', François Anctil, 18 May 2026
- AC1: 'Reply on RC1', Dikraa Khedhaouiria, 20 Jul 2026
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RC2: 'Comment on egusphere-2026-620', Anonymous Referee #2, 08 Jul 2026
General comment:
This paper presents an important 45-year high-resolution North American precipitation reanalysis (CaSR v3.2/CaPA-24h) that will be useful for hydrological, climatological, and impact studies. The methodology is generally well documented, and the new version demonstrates several important improvements over previous releases, particularly in reducing background-field biases and improving performance in data-sparse regions. The evaluation is comprehensive, including comparisons with CaSR v2.1, ERA5-Land, PRISM, and station-based leave-one-out verification. The authors also discuss the remaining limitations. Overall, I think the paper is well organised, and the results are convincing. I have several minor concerns regarding the presentation of the methodology, the interpretation of the results, and the wording of some conclusions.
Comments:- The manuscript repeatedly refers to PRISM as an independent evaluation dataset. However, PRISM is itself a gauge-based interpolation product and likely relies on many of the same station observations assimilated by CaPA. Therefore, the comparison may not be fully independent, and part of the agreement between CaPA and PRISM could arise from their shared observational constraints. I suggest that the authors clarify the degree of independence between the two datasets and discuss this limitation.
- A related concern applies to the leave-one-out (LOO) verification. In dense gauge networks, particularly over eastern North America, spatial correlations can extend well beyond the spacing of individual stations. It would be helpful if the authors could discuss how much this residual dependence may still influence the reported verification scores.
- The evaluation of extreme precipitation indices is based on the 2002–2018 period, which provides only 17 annual samples for metrics such as Rx1day at each grid cell. Such a limited sample size may introduce considerable sampling uncertainty into KGE and related statistics.
- Since the dataset is intended as a 45-year climate record (1980–2024), I wonder whether the authors have evaluated its temporal homogeneity across the major observing-system transition around 2000. It would be helpful to discuss whether these changes could introduce artificial shifts or affect long-term trend analyses.
- In Line 42, the manuscript states that “precipitation is not directly assimilated in atmospheric reanalyses,” but the following sentence mentions that systems such as CFSR ingest observation-based precipitation products. This wording may be confusing for readers who are not familiar with modern reanalysis systems. I suggest revising this paragraph.
Citation: https://doi.org/10.5194/egusphere-2026-620-RC2 - AC2: 'Reply on RC2', Dikraa Khedhaouiria, 20 Jul 2026
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-620', François Anctil, 18 May 2026
This paper, submitted by Khedhaouiria et al., explains the methodology behind the recent Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset for North America and assesses its overall quality
General comment:
Developing a climate reanalysis is a task that is both meticulous and far-reaching, given the sheer volume of supporting data and the many pitfalls involved. Not to mention that Canada is a vast country, large parts of which are under-instrumented, requiring a tailored strategy. The work presented here, which focuses on total precipitation, is the result of ongoing improvements to Canadian hydroclimatic products. These products are particularly useful to the operational hydrology community.
Major comments:
It would have been helpful to state clearly from the outset that the database compiled relates to total precipitation, without attempts to identify the phase of the precipitation. I do not dispute the decision not to distinguish between liquid and solid precipitation, but rather the decision not to state this clearly or to justify it.
Section 6.1 – The manuscript draws on numerous databases, and their use is not always clear to the reader. This is particularly the case here, where numerous databases are evaluated and compared without specifying the source of the observations used as a reference. We must assume that these are the ones described in Figure 1, but they are then described as having been used for assimilation, not for evaluation. The same applies in Appendix A2: Details of assimilated data sets. In fact, the information is provided further on, in Section 6.2, line 364.
Section 6.4 – If I understand correctly, metrics are calculated for each grid point for the 17-year period from 2002 to 2018 in the PRISM database. Since the selected indicators are annual values, the time series for each point consists of 17 values. I am not sure that it is convincing to present KGE on such short time series. In this respect, in Figure 9, I have more confidence in the assessment of bias and correlation than in that of variability and therefore the KGE. Personally, I would not present the KGE and variability. It seems to me that the issue of the length of the time series deserves at least a detailed comment. What is written on line 482 seems insufficient to me.
Figure 11 – The diurnal cycle is essentially a local process. Is there an issue with combining values across large areas? Furthermore, the decision to present times in UTC complicates interpretation, as not everyone is familiar with how to perform the conversion.
The work presented here is partly motivated by hydrological applications. Indeed, the success of hydrological models depends largely on the quality of historical precipitation data – although this is not the only challenge they face. However, this precipitation alone is insufficient. At a minimum, an air temperature database is also required to clarify the phase of precipitation and to model the melting of snow accumulated on the ground – essential components of hydrology in a northern climate such as Canada’s. Although this is not the focus of this manuscript, it would be useful for potential users if this issue were addressed in the discussion or conclusion, for example by suggesting air temperature databases (and why not other hydrometeorological variables) compatible with CaSR v3.2.
Minor comments:
Line 50 – Please write out ‘NWP’ in full when it first appears
Line 135 – A brief note on the annual fluctuation in Figure 1 would be helpful. The explanation comes much later in the text, at lines 599 and 617. Also, Figure 1 would be easier to interpret if the y-axis started at zero.
Line 300 – The equations describing these metrics could be placed in the appendix with the others already there.
Figure 14 – I am not sure that this figure is strictly necessary.
Ligne 645 – Is it possible to provide an appreciation of the number of stations provided to the study by these different networks?
Citation: https://doi.org/10.5194/egusphere-2026-620-RC1 - AC1: 'Reply on RC1', Dikraa Khedhaouiria, 20 Jul 2026
-
RC2: 'Comment on egusphere-2026-620', Anonymous Referee #2, 08 Jul 2026
General comment:
This paper presents an important 45-year high-resolution North American precipitation reanalysis (CaSR v3.2/CaPA-24h) that will be useful for hydrological, climatological, and impact studies. The methodology is generally well documented, and the new version demonstrates several important improvements over previous releases, particularly in reducing background-field biases and improving performance in data-sparse regions. The evaluation is comprehensive, including comparisons with CaSR v2.1, ERA5-Land, PRISM, and station-based leave-one-out verification. The authors also discuss the remaining limitations. Overall, I think the paper is well organised, and the results are convincing. I have several minor concerns regarding the presentation of the methodology, the interpretation of the results, and the wording of some conclusions.
Comments:- The manuscript repeatedly refers to PRISM as an independent evaluation dataset. However, PRISM is itself a gauge-based interpolation product and likely relies on many of the same station observations assimilated by CaPA. Therefore, the comparison may not be fully independent, and part of the agreement between CaPA and PRISM could arise from their shared observational constraints. I suggest that the authors clarify the degree of independence between the two datasets and discuss this limitation.
- A related concern applies to the leave-one-out (LOO) verification. In dense gauge networks, particularly over eastern North America, spatial correlations can extend well beyond the spacing of individual stations. It would be helpful if the authors could discuss how much this residual dependence may still influence the reported verification scores.
- The evaluation of extreme precipitation indices is based on the 2002–2018 period, which provides only 17 annual samples for metrics such as Rx1day at each grid cell. Such a limited sample size may introduce considerable sampling uncertainty into KGE and related statistics.
- Since the dataset is intended as a 45-year climate record (1980–2024), I wonder whether the authors have evaluated its temporal homogeneity across the major observing-system transition around 2000. It would be helpful to discuss whether these changes could introduce artificial shifts or affect long-term trend analyses.
- In Line 42, the manuscript states that “precipitation is not directly assimilated in atmospheric reanalyses,” but the following sentence mentions that systems such as CFSR ingest observation-based precipitation products. This wording may be confusing for readers who are not familiar with modern reanalysis systems. I suggest revising this paragraph.
Citation: https://doi.org/10.5194/egusphere-2026-620-RC2 - AC2: 'Reply on RC2', Dikraa Khedhaouiria, 20 Jul 2026
Peer review completion
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Cited
2 citations as recorded by crossref.
- Spatial pattern regression for meteorological fields interpolation V. Houssou & J. Carreau https://doi.org/10.5194/hess-30-5791-2026
- Enhancing runoff-infiltration partitioning in the SVS land surface model improves streamflow simulations under frozen soil conditions B. Bouchard et al. https://doi.org/10.5194/hess-30-4823-2026
Dikraa Khedhaouiria
Nicolas Gasset
Vincent Fortin
Milena Dimitrijevic
Maxim Bulat
Xihong Wang
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(17697 KB) - Metadata XML
-
Supplement
(11548 KB) - BibTeX
- EndNote
- Final revised paper
This paper, submitted by Khedhaouiria et al., explains the methodology behind the recent Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset for North America and assesses its overall quality
General comment:
Developing a climate reanalysis is a task that is both meticulous and far-reaching, given the sheer volume of supporting data and the many pitfalls involved. Not to mention that Canada is a vast country, large parts of which are under-instrumented, requiring a tailored strategy. The work presented here, which focuses on total precipitation, is the result of ongoing improvements to Canadian hydroclimatic products. These products are particularly useful to the operational hydrology community.
Major comments:
It would have been helpful to state clearly from the outset that the database compiled relates to total precipitation, without attempts to identify the phase of the precipitation. I do not dispute the decision not to distinguish between liquid and solid precipitation, but rather the decision not to state this clearly or to justify it.
Section 6.1 – The manuscript draws on numerous databases, and their use is not always clear to the reader. This is particularly the case here, where numerous databases are evaluated and compared without specifying the source of the observations used as a reference. We must assume that these are the ones described in Figure 1, but they are then described as having been used for assimilation, not for evaluation. The same applies in Appendix A2: Details of assimilated data sets. In fact, the information is provided further on, in Section 6.2, line 364.
Section 6.4 – If I understand correctly, metrics are calculated for each grid point for the 17-year period from 2002 to 2018 in the PRISM database. Since the selected indicators are annual values, the time series for each point consists of 17 values. I am not sure that it is convincing to present KGE on such short time series. In this respect, in Figure 9, I have more confidence in the assessment of bias and correlation than in that of variability and therefore the KGE. Personally, I would not present the KGE and variability. It seems to me that the issue of the length of the time series deserves at least a detailed comment. What is written on line 482 seems insufficient to me.
Figure 11 – The diurnal cycle is essentially a local process. Is there an issue with combining values across large areas? Furthermore, the decision to present times in UTC complicates interpretation, as not everyone is familiar with how to perform the conversion.
The work presented here is partly motivated by hydrological applications. Indeed, the success of hydrological models depends largely on the quality of historical precipitation data – although this is not the only challenge they face. However, this precipitation alone is insufficient. At a minimum, an air temperature database is also required to clarify the phase of precipitation and to model the melting of snow accumulated on the ground – essential components of hydrology in a northern climate such as Canada’s. Although this is not the focus of this manuscript, it would be useful for potential users if this issue were addressed in the discussion or conclusion, for example by suggesting air temperature databases (and why not other hydrometeorological variables) compatible with CaSR v3.2.
Minor comments:
Line 50 – Please write out ‘NWP’ in full when it first appears
Line 135 – A brief note on the annual fluctuation in Figure 1 would be helpful. The explanation comes much later in the text, at lines 599 and 617. Also, Figure 1 would be easier to interpret if the y-axis started at zero.
Line 300 – The equations describing these metrics could be placed in the appendix with the others already there.
Figure 14 – I am not sure that this figure is strictly necessary.
Ligne 645 – Is it possible to provide an appreciation of the number of stations provided to the study by these different networks?