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 Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

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.

Anomalous weather resulting in Temperature and Precipitation extremes occurs almost every day somewhere in Canada. 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. Virtual Climate stations correspond with the city pages of weather.gc.ca. This data provides the daily extremes of record for Temperature for each day of the year. Daily elements include: High Maximum, Low Maximum, High Minimum, Low Minimum.

Climate Normals and Averages are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its Climate Normals for as many locations and as many climatic characteristics as possible. The Climate Normals, Averages and Extremes offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

This collection of flood susceptibility products captures temporal patterns of historic flood events from 2000 to 2023, derived from flood susceptibility maps. The layers are designed to support decision-making by providing easily interpretable information for planning, screening, and other management purposes.This collection contains several datasets which explore the suite of rasters from 2000 to 2023:- Extreme wet and extreme dry years and the associated average flood susceptibility, processed by NHN WU, as found by Generalized Extreme Value (GEV) analysis- Flood susceptibility envelope, which is derived from the above wet and dry years- Trend slope, for each pixel the value for each year is extracted and the linear trend is computed, greater than 1000 is increasing flood susceptibility and below 1000 is decreasing flood susceptibility. - Trend Class, as identified by wet and dry scores, applied to NHN WU- Current flood susceptibility, which represents the estimated average value of present-day flood susceptibility- Current interquartile range, which provides and indication of the temporal variability of a given pixel under the current regime.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- **[Collection - Flood Susceptibility Mapping](https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)**- **[Historic - Flood Susceptibility Mapping](https://open.canada.ca/data/en/dataset/ea1384df-bf4a-4743-97bb-870dc43f8d77)**- **[Future - Flood Susceptibility Mapping](https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)**

This polygon layer visualizes actual observed precipitation polygons (classed by amounts) from the HRDPA product, offering a clear map-based depiction of recent rainfall or snowfall distribution. Observation periods depicted are past 1 day, past 3 days and past 7 days.This polygon layer is generated by taking HRDPA’s gridded precipitation data (6h, 24h, or multi-day accumulations) and grouping them into precipitation ranges, then polygonizing. Each feature shows how much precipitation truly fell in that zone. This is essential for event verification against forecasts, analyzing localized extremes, and updating water resource or flood models with real observed input.

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.

Extreme water level along the marine coastline is a result of a combination of storm surge, tides, and ocean waves. Future projections of climate change in the marine environment indicate that rising sea level and declining sea ice will cause changes in extreme water levels, which will impact Canada's coastlines and the infrastructure in these areas. Understanding these changes is essential for developing adaptation strategies that can minimize the harmful effects that may result.CAN-EWLAT is a science-based planning tool for climate change adaptation of coastal infrastructure related to future water-level extremes and changes in wave climate. The tool includes two main components: 1) vertical allowance and 2) wave climate. CAN-EWLAT was developed primarily for DFO Small Craft Harbours (SCH) locations, but it should prove useful for coastal planners dealing with infrastructure along Canada’s ocean coastlines.Cite this data as: Greenan B. Canadian Extreme Water Level Adaptation Tool (CAN-EWLAT) Published June 2022. Oceans Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in maximum temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected change in maximum temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected maximum temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of maximum temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled maximum temperature (°C) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.