Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study
Study region: Canada Study focus: High-resolution bias-corrected daily precipitation projections are of great value for regional climate impact assessment. The study evaluates the performance of bias correction techniques in developing high-resolution daily precipitation simulations over Canada. Qua...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825000473 |
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| Summary: | Study region: Canada Study focus: High-resolution bias-corrected daily precipitation projections are of great value for regional climate impact assessment. The study evaluates the performance of bias correction techniques in developing high-resolution daily precipitation simulations over Canada. Quantile Delta Mapping (QDM) and Scaled Distribution Mapping (SDM) are employed to bias correct Coupled Model Intercomparison Project phase 6 (CMIP6) general circulation models (GCMs). New hydrological insights for the region: CMIP6 raw and bias corrected GCMs demonstrate alignment with observations. Raw GCMs overestimate middle and high quantiles and show better performance in winter than in summer. QDM and SDM substantially enhance the performance of individual GCMs, which reduces RMSE by 26 % and 21 %, and shows satisfactory skill in capturing seasonal cycle and spatial variability as well as reproducing probability distribution of daily series and extreme events. The ensemble means of models are skillful for frequent precipitation values but overestimate low quantiles and underestimate high quantiles at a daily scale. Bias corrected ensemble means demonstrate superior performance for the whole distribution including the high and low extremes. SDM outperforms QDM with extreme bias reduced by 85 % and 78 % compared to raw GCMs. The best performing model is SDM corrected ensemble mean. The comprehensive evaluation of daily precipitation bias correction with CMIP6 GCMs over Canada contributes to further climate impact assessment around the world. |
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| ISSN: | 2214-5818 |