Statistically downscaled climate scenarios and indices from CMIP6 global climate models

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Environment and Climate Change Canada has a mandate to provide Canadians with past and future climate change information for climate impact assessments, adaptation planning and mitigation policy development. There is a growing demand in Canada and internationally for global climate model (GCM) projections to be downscaled at high spatial resolutions. ECCC’s 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. These have been widely used and are referenced in Canada’s Changing Climate Report.Reference1 The CMIP5 downscaled scenarios are available on ECCC’s Canadian Climate Data and Scenarios website and PCIC’s climate change scenarios data portal.

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). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs).Reference9 Statistically downscaled datasets have been produced from 26 CMIP6 GCMs (Table 2) and under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5). The first set of scenarios was produced using the same downscaling method (BCCAQv2)Reference3Reference12 and downscaling target data (NRCANmet)Reference8 as the CMIP5-based downscaled scenarios. The second scenario dataset uses a new multivariate method (MBCn) and a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Downscaled daily maximum and minimum temperatures and daily precipitation 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.

Table 1. Main characteristics.

Dataset names

CanDCS-U6: Canadian Downscaled Climate Scenarios–Univariate method from CMIP6

CanDCS-M6: Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6

Variables and unit

Daily maximum temperature (°C)

Daily minimum temperature (°C)

Daily precipitation (mm)

Geographic area


Spatial resolution

1/12° (10km) grid resolution

Time Period

1950 to 2100

Historical emissions: 1950-2014

Future emissions scenarios: 2015-2100

Future emissions scenarios




Climate indices

31 climate indices (Table 3)

Downscaling methodology and data

Proper usage of statistically downscaled data requires a basic understanding of how climate models simulate the climate system and how outputs from climate models are combined with historical observations to create downscaled climate scenarios. Uncertainties are present in each step of the climate modelling and downscaling chain and it is recommended that users familiarize themselves with the basic principles of climate modelling and downscaling before using these data.Reference4

Statistically downscaled datasets have been constructed using output from 26 CMIP6 GCMs that are available at the Earth System Grid Federation (ESGF) Data Nodes, including 10 initial-condition members of the CanESM5 model produced by the Canadian Centre for Climate Modelling and AnalysisReference10 and one run from each of 25 other CMIP6 models (Table 2).


Bias Correction/Constructed Analogues with Quantile mapping (BCCAQ) is a hybrid downscaling algorithm that combines downscaling by BCCA (Bias Correction/Constructed Analogues)Reference7 with quantile mapping. The use of Quantile Delta Mapping (QDM)Reference3, a change-preserving form of quantile mapping, distinguishes BCCAQv2 from the previous version of the algorithm. Details are provided by Werner and Cannon (2016).Reference 12 Code implementing the BCCAQv2 method is also available from the Comprehensive R Archive Network (CRAN) repository via the R package “ClimDown”.Reference5

Daily minimum temperature (°C), daily maximum temperature (°C), and daily precipitation (mm/day) outputs from 26 CMIP6 GCMs were downscaled using the BCCAQv2 algorithm. Historical 1/12° (~10km) gridded NRCANmet dataset (Australian National University thin-plate smoothing Splines methodology (ANUSPLINv1)) of daily minimum temperature, maximum temperature and precipitation for CanadaReference8 were used as the respective downscaling “targets” (training data used to calibrate BCCAQv2) so that the historical downscaled climate model output has statistical characteristics that resemble those of NRCANmet as closely as possible during the 1951-2010 period. Once calibrated for a particular climate model and variable for the historical 1951-2010 reference period, BCCAQv2 is applied to the climate simulations of that variable for the selected model under three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). This process is repeated separately for each variable and model, and hence is termed a “univariate” downscaling scheme.


A univariate downscaling method, which downscales each variable independently, can omit or poorly represent important phenomena that are characterized by combinations of temperature and precipitation. A multivariate approach to downscaling, while more computationally expensive, can maintain inter-variable dependencies and allows for analyses of multi-variable phenomena. N-dimensional Multivariate Bias Correction (MBCn)Reference2 is a multivariate downscaling and bias correction methodology that preservers inter-variable dependencies and the statistical characteristics of a continuous multivariate distribution. The MBCn method uses a modified form of quantile delta mapping (QDM) that is able to work with multiple variables and preserve the statistical properties between each variable.

During the creation of the CanDCS-M6 dataset, PCIC identified sporadic anomalously large daily maximum temperature values in three of the contributing 26 GCMs. PCIC determined that the anomalous values were caused by errors in the land surface and atmospheric components shared by models. To prevent the errors from propagating into the downscaled dataset and avoid introducing missing values by omitting the anomalous values, PCIC implemented an outlier detection and correction methodology which replaces anomalous temperatures with values robustly interpolated from surrounding grid cells. The three models with known errors are, the two Met Office Hadley Centre (MOHC) models HadGEM3-GC31-LL and UKESM1-0-LL, and KACE-1-0-G from the Korean Meteorological Administration (KMA). PCIC has re-run simulations for the three models using the CanDCS-U6 methodology and corrected the original datasets. The updated CanDCS-U6 datasets replaced the ones already on CCDS on August 25th, 2023.

The CanDCS-M6 dataset includes the same variables, GCMs, and grid resolution as described for CanDCS-U6 but implements a new target dataset. ANUSPLINv1 has a known dry precipitation bias on the west coast of Canada, to address this, the CanDCS-M6 target dataset blends several new observational gridded datasets. The new blended downscaling target dataset (hereafter referred to as PCIC-Blend) combines daily values of precipitation, and maximum and minimum temperature from the PCIC meteorology for Northwest North America dataset (PNWNAmet)Reference11 with daily precipitation from the new Adjusted Daily Precipitation gridded dataset (ANUSPLIN Adj)Reference6, and daily maximum and minimum temperatures from the version 2 ANUSPLIN dataset (ANUSPLIN v2) Reference8.

The datasets are blended using a weighted average applied over a limited common region, with a diagonal along the eastern side Rocky Mountains acting as the blending region to combine the PNWNAmet (representing western Canada) and the two ANUAPLIN datasets (representing central and eastern Canada). The PCIC-Blend dataset therefore makes use of the updates and improved performances of multiple new products, improving the representation of precipitation in western Canada. The incorporation of PNWNAmet provided the opportunity to extend data into the western US down to 41°N. A continuous dataset across the US border along the west coast allows the CanDCS-M6 dataset to cover the extent of the Columbia river basin, identified as a key watershed in the hydrologic impacts theme.

Unlike univariate indices, which are derived exclusively from either daily precipitation, maximum temperature, or minimum temperature, multivariate indices are calculated using some combination of the three variables. Phenomena which result from the combination of temperature and precipitation conditions such as drought, wildfires, and snow, require effective simulation of the dependencies between temperature and precipitation, which a univariate approach cannot accomplish.

Table 2. List of CMIP6 global climate models used in the statistically downscaled multi-model ensembles.

Institution Model Name Realization
CSIRO-ARCCSS (Australia) ACCESS-CM2 r1i1p1f1
CSIRO (Australia) ACCESS-ESM1-5 r1i1p1f1
Beijing Climate Center (China) BCC-CSM2-MR r1i1p1f1
Canadian Centre for Climate Modelling and Analysis (Canada) CanESM5 r1i1p2f1 ~ r10i1p2f1
Euro-Mediterranean Centre for Climate Change (Italy) CMCC-ESM2 r1i1p1f1
CNRM-CERFACS (France) CNRM-CM6-1 r1i1p1f2
CNRM-CERFACS (France) CNRM-ESM2-1 r1i1p1f2
EC-Earth-Consortium (Europe) EC-Earth3 r4i1p1f1
EC-Earth-Consortium (Europe) EC-Earth3-Veg r1i1p1f1
Institute of Atmospheric Physics (China) FGOALS-g3 r1i1p1f1
NOAA-Geophys. Fluid Dyn. Lab (USA) GFDL-ESM4 r1i1p1f1
Met Office Hadley Centre and NERC (UK) HadGEM3-GC31-LL r1i1p1f3
Institute for Numerical Mathematics (Rus.) INM-CM4-8 r1i1p1f1
Institute for Numerical Mathematics (Rus.) INM-CM5-0 r1i1p1f1
Institut Pierre-Simon Laplace (France) IPSL-CM6A-LR r1i1p1f1
National Institute of Meteo. Sciences and Korea Meteo. Administration (Korea) KACE-1-0-G r2i1p1f1
Korea Institute of Ocean Science and Technology (Korea) KIOST-ESM r1i1p1f1
University of Tokyo JAMSTEC, NIES, and AORI (Japan) MIROC6 r1i1p1f1
University of Tokyo JAMSTEC, NIES, and AORI (Japan) MIROC-ES2L r1i1p1f2
Max Planck Institute for Meteo. (Germany) MPI-ESM1-2-HR r1i1p1f1
Max Planck Institute for Meteo. (Germany) MPI-ESM1-2-LR r1i1p1f1
Meteorological Research Institute (Japan) MRI-ESM2-0 r1i1p1f1
Norwegian Climate Center (Norway) NorESM2-LM r1i1p1f1
Norwegian Climate Center (Norway) NorESM2-MM r1i1p1f1
Research Center for Env. Changes (Taiwan) TaiESM1 r1i1p1f1
Met Office Hadley Centre and NERC (UK) UKESM1-0-LL r1i1p1f2

Climate indices

For each downscaled GCM, 31 climate indices have been calculated (Table 3). The climate indices include 27 Climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI)Reference13 and 4 additional indices that are slightly modified from the Climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the indices.

Table 3. List of 31 climate indices (27 indices recommended by ETCCDI and 4 additional indices considered in this project (in bold)).

Number Label Index name Unit
1 FD Number of frost days days
2 SU Number of summer days (> 25°C) days
3 SU30 Number of summer days (> 30°C) days
4 ID Number of icing days days
5 TR Number of tropical nights days
6 GSL Growing season length days
7 TXx Monthly maximum value of daily maximum temperature (TX) °C
8 TNx Monthly maximum value of daily minimum temperature (TN) °C
9 TXn Monthly minimum value of TX °C
10 TNn Monthly minimum value of TN °C
11 TN10p Percentage of days when TN < 10th percentile %
12 TX10p Percentage of days when TX < 10th percentile %
13 TN90p Percentage of days when TN > 90th percentile %
14 TX90p Percentage of days when TX > 90th percentile %
15 WSDI Warm spell duration index days
16 CSDI Cold spell duration index days
17 DTR Daily temperature range °C
18 Rx1day Monthly maximum 1-day precipitation mm
19 Rx2day Monthly maximum consecutive 2-day precipitation mm
20 Rx5day Monthly maximum consecutive 5-day precipitation mm
21 SDII Simple precipitation intensity index mm
22 R1mm Annual count of days when daily precipitation amount (PRCP) ≥ 1mm days
23 R10mm Annual count of days when PRCP ≥ 10mm days
24 R20mm Annual count of days when PRCP ≥ 20mm days
25 CDD Maximum length of dry spell, maximum number of consecutive days with PRCP < 1mm days
26 CWD Maximum length of wet spell, maximum number of consecutive days with PRCP ≥ 1mm days
27 R95p Annual total accumulation of precipitation on wet days (PRCP > 95th percentile) mm
28 R99p Annual total accumulation of precipitation on very wet days (PRCP > 99th percentile) mm
29 PRCPTOT Annual total precipitation in wet days mm
30 R95day Number of days where daily precipitation exceeds the 95th percentile days
31 R99day Number of days where daily precipitation exceeds the 99th percentile days

File formats

Downscaled simulations have been organized into separate directories containing one simulation for each GCM, variable, realization, and shared socioeconomic pathway. For example, the downscaled simulation of CanDCS-U6 daily precipitation from the first realization (r1i1p1f1) of ACCESS-CM2 following the SSP1-2.6 pathway is contained within the directory:


In view of the large file size of the downscaled simulations (57 Gb per file), each complete simulation has been split into 5-year subsets. Each 5-year file is labelled following the same filename format to describe the simulation and time interval contained within the file. For example, the first file within the directory listed above is the following:


Components of the filename format include:

Each simulation directory contains one 6-year file (1950-1955, 2.3 Gb) and 29 5-year files (1.9 Gb each) that together comprise the 151-year Canada-wide downscaled simulation for that GCM, variable, realization, and socioeconomic pathway. The grid label identifier denotes whether GCM simulations are provided using the native grid of the model ("gn") or have been regridded ("gr") to another primary grid by the modelling centre. Instances where simulations from a GCM are provided at more than one grid resolution are indicated using an integer designation (e.g. "gr2").

Files of climate indices are labelled following the same filename format. For example, the first file within the Climdex directory of ACCESS-CM2 is the CDD at annual (ann) time resolution is the following:

cddETCCDI_ann_BCCAQ2v2+ANUSPLIN300_ACCESS-CM2_historical+ssp126_ r1i1p1f1_1950-2100.nc

Components of the filename format for the climate indices include:

Dataset licence

Open Government Licence - Canada (http://open.canada.ca/en/open-government-licence-canada)

Individual model datasets and all related derived products, including the multi-model ensembles, are subject to the terms of use of the source organization.

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