Version 2 of the Canadian homogenized wind speed (CanHomW V2) datasets contain homogenized monthly and daily mean wind speed data series for the period 1953-2023 for 154 long-term stations in Canada. As detailed in Wang et al. (2025), the monthly dataset (CanHomW mlyV2) was produced using a modified version of the comprehensive semi-automatic changepoint detection procedure developed by Wang et al. (2023), which includes homogeneity testing with and without using a reference series. To produce the homogenized daily wind speed dataset (CanHomW dlyV2), daily wind speed data series were tested to find the most probable day of an artificial change detected in the corresponding monthly data series whenever the exact date of change in the daily series is undocumented. The Twentieth Century Reanalysis 20CRv3 ensemble-mean series of monthly wind speeds (Slivinski et al., 2019), 49 monthly mean geostrophic wind speeds derived from surface pressure data, and up to four nearest stations’ data series were used as reference in the homogeneity tests. However, no reference was used to adjust the data series to diminish the detected inhomogeneities. This is because the density of long-term wind observing stations in Canada is too low to find a representative reference series to use for estimating reliable adjustments to homogenize the data series. A modified version of the quantile matching (QM) adjustment method with no reference (Wang et al. 2010) was used to homogenize the data series. The QM method adjusts the whole distribution of the data in one segment to match another, rather than just adjust the mean. The modifications here include an approach to prevent having unphysical negative values in the homogenized wind speed series (see Wang et al. 2025 for more details). Despite the higher uncertainty arising from applying adjustments estimated without using a reference series, compared to when applying adjustments estimated using a representative reference series (if such a reference were available), this is the best (i.e., most usable for trend assessment) Canadian homogenized wind speeds dataset that has been produced to date. For transparency and traceability, the raw (original/unhomogenized) data series along with the station joining information are included in the CanHomW datasets. Trends in the raw and homogenized data are analyzed and discussed in Wang et al. (2025). References: Wang, X.L., Y. Feng, V. Isaac, F. W. Zwiers, L. A., Vincent, and M. Hartwell, 2025: Observed Surface Wind Speed Trends Inferred from Homogenized in Situ Data and Reanalysis Datasets. Atmosphere-Ocean iFirst article, 2025, 1-17, https://doi.org/10.1080/07055900.2025.2570920. Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., 36, 7957-7971. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., Chen, H., Wu, Y., Feng, Y., & Pu, Q. (2010). New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series. Journal of Applied Meteorology and Climatology, 49(12), 2416–2436. https://doi.org/10.1175/2010JAMC2376.1. Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598.

Overview CanGridP mlyV2 provides high-resolution gridded monthly precipitation amounts for Canada, based on homogenized station observations (Wang et al., 2026). This dataset supports climate analysis, trend assessment, and applications such as model validation, downscaling studies, and climate monitoring. Methodology This dataset was produced using a gridding method (Abbasnezhadi & Wang, 2024) that applies ordinary kriging separately to: • station climate normals (1961–1990) • station climate relative anomalies (expressed as a percentage of the normal) The gridded normals and anomalies are then combined to obtain final gridded monthly total precipitation amounts (Wang et al., 2026). Version 2 applies this method to the Canadian Homogenized Monthly Precipitation Version 2 (CanHomP mlyV2) dataset, which includes 425 stations across Canada (Wang et al. 2026). All stations had sufficient data for calculating monthly normals for 1961-1990 and were used for producing the gridded dataset. CanGridP mlyV2 represents Canada’s precipitation trend reasonably well since 1949, and southern Canada’s trend since 1916, despite changes in data availability over time (Wang et al., 2026). Source Data: CanHomP mlyV2 CanHomP mlyV2 was developed for climate trend analysis and include long-term monthly total precipitation series for 425 stations across Canada (Wang et al., 2026; Wang et al. 2023). Key improvements in Version 2 over its predecessor, CanHomPmlyV1 (Wang et al., 2023), include: • Expanded and improved source datasets, incorporating adjusted data from automated gauge stations and the Collaborative Rain, Hail and Snow (CoCoRaHS) network (https://www.cocorahs.org/canada.aspx) for recent decades. • Additional quality control procedures to remove false zeros in station data records for data-sparse regions or periods. • Enhanced changepoint detection, including tests on both untransformed and log-transformed data, and improved methods for identifying station-joining and variance inhomogeneities. • Use of more complete metadata for better accuracy. • Improved adjustment procedures to eliminate inhomogeneity by adjusting untransformed data series. • Development of homogenized daily precipitation series consistent with corresponding monthly totals (Wang & Feng, 2026). The key processing steps for both V1 and V2 datasets are available at [https://catalogue.ec.gc.ca/geonetwork/srv/eng/catalog.search#/metadata/1108608d-5a35-4cbd-b3be-20929befddf7]. The homogenization process employs the Quantile-Matching method (Wang et al., 2026; Wang et al., 2023; Wang et al., 2010), which ensures that distribution changes—including variance shifts—at identified changepoints are corrected. Differences from CanGRD • CanGridP: Gridded precipitation amounts on an approximately 10-km EASE grid (grid box area: ~100 km²) • CanGRD: Gridded anomalies of precipitation amount on an approximately 50-km EASE grid (grid box area: ~2,500 km²) References Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean. https://doi.org/10.1080/07055900.2026.2617861. Abbasnezhadi, K. and X. L. Wang, 2024: Comparison of gridding methods for precipitation over Canada and assessment of station/data density effects on gridding results. Atmos.-Ocean, 62(4), 320-346, https://doi.org/10.1080/07055900.2024.2394829. Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., 36, 7957-7971. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmos.-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. Cheng, V. Y. S., Wang, X.L., and Y. Feng, 2024: A quality control system for historical in situ precipitation data. Atmosphere-Ocean, 62(4), 271-287, https://doi.org/10.1080/07055900.2024.2394836. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1.

Canadian Homogenized Surface Air Temperature – Version 3.1 (CanHomT V3.1) This dataset is an update of the Third Generation Homogenized Temperature dataset (Vincent et al. (2020), extended through 2023. It was developed for climate trend analysis and includes long-term daily maximum, minimum, and mean temperature series for 780 stations (776 locations) in Canada. The key enhancement in V3.1 is the inclusion of both Original and Adjusted data, together with their associated data flags and the source station climate ID, providing improved traceability, reproducibility, and transparency. Key processing steps include: • Merging observations from nearby sites to create long records • Quality control of data • Adjusting daily minimum temperatures since July 1961 for the observing-time (climatological day definition) change at 96 principal stations (Vincent et al., 2009) • Detecting non-climatic shifts in annual and seasonal average temperature data series using homogeneity tests with reference and station metadata. • Applying Quantile-Matching (QM) adjustments with reference data (including parallel observations) to correct non-climatic changes (Vincent et al., 2018; Wang et al., 2010). Parallel observations —simultaneous measurements from old and new instruments or locations—were used whenever available because they provide the most reliable basis for data homogenization. Adjustments made without suitable reference data carry greater uncertainty, and this increased uncertainty should be quantified and clearly communicated (WMO, 2020). The QM method adjusts the entire distribution of data in one segment to match another (Wang et al., 2026; Vincent et al., 2018; Wang & Feng, 2013; Wang et al. 2010), ensuring that distribution changes—including variance shifts—at identified changepoints are homogenized. The gridded version of monthly CanHomT V3.1, called CanGridT mlyV3.1 [https://open.canada.ca/data/en/dataset/781e02cc-6c1b-462e-b61b-f96c607b23bd], was found to represent Canada’s warming trend reasonably well since 1900, despite changes in data availability over time (Wang et al., 2026, see their Figure S3). References Vincent, L.A., M.M. Hartwell and X.L. Wang, 2020: A Third Generation of Homogenized Temperature for Trend Analysis and Monitoring Changes in Canada’s Climate. Atmosphere-Ocean, 58(3), 173–191. https://doi.org/10.1080/07055900.2020.1765728. Vincent, L.A., E.J. Milewska, R. Hopkinson and L. Malone, 2009: Bias in minimum temperature introduced by a redefinition of the climatological day at the Canadian synoptic stations. J. Appl. Meteor. Climatol, 48, 2160-2168. DOI: 10.1175/2009JAMC2191.1. Vincent, L.A., E.J. Milewska, X. L. Wang, and M. M. Hartwell, 2018. Uncertainty in homogenized daily temperatures and derived indices of extremes illustrated using parallel observations in Canada, Intl. J. Climatol., 38:2, 692-707. DOI: 10.1002/JOC.5203. Wang, X. L. and Y. Feng, published online July 2013: RHtestsV4 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 28 pp. [Available online at https://github.com/ECCC-CDAS] DOI: 10.13140/RG.2.2.17309.17125. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1. WMO. (2020). Guidelines on Homogenization (2020 edition). World Meteorological Organization. WMO-No. 1245. https://library.wmo.int/records/item/57130-guidelines-on-homogenization?offset=1. Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of the Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean, 1–16, https://doi.org/10.1080/07055900.2026.2617861.

Canadian Homogenized Precipitation – Version 2 (CanHomP V2) The CanHomP V2 datasets were developed for climate trend analysis and include long-term monthly and daily precipitation series for 425 stations across Canada (Wang et al., 2026; Wang & Feng, 2026). Key improvements in Version 2 over its predecessor, CanHomPmlyV1 (Wang et al., 2023), include: • Expanded and improved source datasets, incorporating adjusted data from automated gauge stations and the Collaborative Rain, Hail and Snow (CoCoRaHS) network (https://www.cocorahs.org/canada.aspx) for recent decades. • Additional quality control procedures to remove false zeros in station data records for data-sparse regions or periods. • Enhanced changepoint detection, including tests on both untransformed and log-transformed data, and improved methods for identifying station-joining and variance inhomogeneities. • Use of more complete metadata for better accuracy. • Improved adjustment procedures to eliminate inhomogeneity by adjusting untransformed data series. • Development of homogenized daily precipitation series consistent with corresponding monthly totals (Wang & Feng, 2026). Key processing steps for both V1 and V2 include: • Merging observations from nearby sites to create long records, primarily using updated Adjusted Daily Rainfall and Snowfall data corrected for known issues such as unrealistic snow-water equivalent conversion, gauge wetting loss, and wind-induced undercatch (Wang et al., 2017). • Infilling data gaps using advanced spatial modeling of available data for the gap period. • Comprehensive quality control of data (Cheng et al., 2024). • Detecting non-climatic shifts using homogeneity tests with reference and station metadata. • Using up to four best neighbor stations as references, along with data derived from advanced spatial modeling of adjusted precipitation (MacDonald et al., 2021) and the Twentieth Century Reanalysis (20CRv3) ensemble-mean series of monthly precipitation (Slivinski et al., 2019). • Applying Quantile-Matching (QM) adjustments without reference data to correct non-climatic changes (Wang et al., 2026; Wang et al., 2023; Wang et al., 2010). Wang et al. (2023) also developed a dynamic QM adjustment method that uses the most strongly correlated homogeneous segment of nearby station data as a reference. They found that when station density is too low to identify suitable reference stations, adjustments without a reference could perform better than those with one, although results are similar for most stations. This approach may not be applicable to precipitation datasets with much higher station density or to variables with greater spatial coherence (e.g., surface air temperature). The QM method adjusts the entire distribution of data in one segment to match another (Wang et al., 2026; Wang et al., 2023; Wang & Feng, 2013; Wang et al., 2010), ensuring that distribution changes—including variance shifts—at identified changepoints are homogenized. Differences from AHCCD adjusted precipitation data • CanHomP V2: Adjusted, gap-filled, homogenized monthly and daily precipitation data for 425 core stations for the period up to 2023. • AHCCD adjusted precipitation: Adjusted but unhomogenized monthly and daily precipitation data for 464 manual stations for the period up to 2012 (Mekis & Vincent, 2011). References Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean, 1-16. https://doi.org/10.1080/07055900.2026.2617861. Wang, X. L., & Feng, Y. (2026). Observed trends in precipitation extreme indices as inferred from a homogenized daily precipitation dataset for Canada. Weather and Climate Extremes, 51, 100860. https://authors.elsevier.com/sd/article/S2212-0947(26)00011-3. Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., 36, 7957-7971. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmos.-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. Cheng, V. Y. S., Wang, X.L., and Y. Feng, 2024: A quality control system for historical in situ precipitation data. Atmosphere-Ocean, 62(4), 271-287, https://doi.org/10.1080/07055900.2024.2394836. Wang, X. L. and Y. Feng, published online July 2013: RHtestsV4 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 28 pp. [Available online at https://github.com/ECCC-CDAS] DOI: 10.13140/RG.2.2.17309.17125. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1. MacDonald, H., D. W. McKenney, X. L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, M. F. Hutchinson, 2021: Spatial Models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. And Climatol., 60, 291-304. DOI: 10.1175/JAMC-D-20-0041.1. Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598. Mekis, E., & Vincent, L. A. (2011). An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49, 163–177. https://doi.org/10.1080/07055900.2011.583910.

A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area (within a 20km radius from the urban center) rather than a single precise location.

Data describing clean growth and climate change projects that have received federal funding since 2015 that feeds into the Climate Action Map. The data include projects that meet Mitigation, Adaptation and Clean Technology objectives. The data include project names and descriptions, funding information, locations, and recipients.

Canada's Changing Climate Report (CCCR) is about how and why Canada’s climate has changed and what changes are projected for the future. Chapter 8 assesses past and future changes in Canada’s weather and climate extremes. These tables are meant to serve as supplementary material for CCCR 2026 Ch. 8. The supplementary tables include regional values and 80% uncertainty ranges of climate extreme indices (https://climate-scenarios.canada.ca/?page=climdex-indices) based on climate model output. Projections are based on CanDCS-M6 (https://climate-scenarios.canada.ca/?page=CanDCS6-indices), an ensemble of 26 downscaled (1/12°) and bias-corrected models from phase six of the Coupled Model Intercomparison Project (CMIP6).

This map shows the projected change in mean precipitation for 2081-2100, with respect to the reference period of 1986-2005 for RCP4.5, expressed as a percentage (%) of mean precipitation in the reference period. The median projected change across the ensemble of CMIP5 climate models is shown. For more maps on projected change, please visit the Canadian Climate Data and Scenarios (CCDS) site: https://climate-scenarios.canada.ca/?page=download-cmip5.

This map shows the projected change in mean precipitation for 2046-2065, with respect to the reference period of 1986-2005 for RCP8.5, expressed as a percentage (%) of mean precipitation in the reference period. The median projected change across the ensemble of CMIP5 climate models is shown. For more maps on projected change, please visit the Canadian Climate Data and Scenarios (CCDS) site: https://climate-scenarios.canada.ca/?page=download-cmip5.

The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.