CMIP6 statistically downscaled climate datasets

Access statistically downscaled climate datasets based on model output from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical downscaling predictors are also available.

What is downscaling? Downscaling is a method of providing global climate model (GCM) output at a finer spatial resolution. Consider a low resolution video where you cannot discern details because each image in the video is composed of a small number of large pixels. In contrast, in a high definition video, you can see even the minute details of the people and background because each image is composed of many small pixels. Downscaling is a similar process, where a low resolution grid of few large pixels is converted to a high resolution one made up of many small pixels. Converting to a higher resolution allows us to discern more details within the image or, in this case, make more spatially detailed future projections for climate. However, it is important to note that a downscaled projection is not necessarily better or more accurate; rather, it provides information at a scale that is more convenient for many applications.

There are two approaches to downscale climate model output: statistical downscaling and dynamical downscaling. In dynamical downscaling, a regional climate model (RCM) provides projections at a higher resolution for a particular region with a global climate model providing the input and drivers from the outside world to the borders of that region. With statistical downscaling, as provided here, a statistical relationship between coarser-scale global climate model output and local historic data from weather stations is derived to produce higher resolution projections.

What datasets are available? The first drop-down menu below provides a link to access statistically downscaled projections of minimum minimum, maximum, and mean temperature and total precipitation. The projections follow the scenarios used in CMIP6 (Coupled Model Intercomparison Project Phase 6), the Shared Socio-economic Pathways (SSPs), which are a set of alternative storylines about how the world might develop, and greenhouse gas emissions might change, over the coming century.

The second drop-down menu provides a link to download statistically downscaled climate indices. An index is a quantitative value that is calculated from climate datasets and provides more targeted information about related to temperature or precipitation. Some examples of climate indices are, the hottest day of each year and driest day of each year. Full lists of the two types of indices provided can be found here (Climdex) and here (agroclimatic).

There are also links at the bottom of the page to CMIP6 daily predictor variables. A predictor variable is output from a global climate model intended for use in the kind of statistical downscaling described above. As well, observational datasets are provided, also for use in statistical downscaling. A total of 26 variables are provided for users with the capability to perform their own statistical downscaling.

To access user-friendly data and resources to inform climate change adaptation visit ClimateData.ca. This site uses data produced by Environment and Climate Change Canada.