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This map shows the projected average change in mean temperature (°C) for 2046-2065, with respect to the reference period of 1986-2005, for RCP2.6. 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.

30-year Average Number of Days with Temperature above 25 °C are defined as the count of the number of climate days during the month where the maximum daily temperature was greater than 25 °C. These values are calculated across Canada in 10x10 km cells.

Multi-model ensembles of mean precipitation based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of mean precipitation (mm/day) are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. 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.

Percent of Average Precipitation represents the accumulation of precipitation for a location, divided by the long term average value. The long term average value is defined as the average amount over the 1981 – 2010 period. Products are produced for the following timeframes: Agricultural Year, Growing Season, Winter Season, as well as rolling products for 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem. The number of large fires refers to the annual number of fires greater than 200 hectares (ha) that occur per units of 100,000 ha. It was calculated per Homogeneous Fire Regime (HFR) zones. These HFR zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications. Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Provided layer: projected number of large fires (>200 ha) across Canada for the long-term (2071-2100) under the RCP 8.5 (continued emissions increases). Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.

The Global Ice-Ocean Prediction System (GIOPS) produces global sea ice and ocean analyses and 10 day forecasts daily. This product contains time-mean sea ice and ocean forecast fields interpolated to two grids. One of the grids is a 0.2° resolution regular latitude-longitude grid covering the global ocean (north of 80° S). The other grid is in north-polar stereographic projection with a 5-km spacing at the standard parallel 60° N and covers the Arctic Ocean and the neighbouring sub-polar seas. Data is available for 50 depths. The data files are in netCDF format and comply with the Climate and Forecast Conventions.

Growing Degree Days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Insect and plant development are very dependent on temperature and the daily accumulation of heat. The amount of heat required to move a plant or pest to the next development stage remains constant from year to year. However, the actual amount of time (days) can vary considerably from year to year because of weather conditions. Base temperatures are a point below which development does not occur for the organism in question. Base 0 temperatures are commonly used for cereals. These values are calculated across Canada in 10x10 km cells.