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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Elsevier
2025-04-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825000473 |
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| author | Xinyi Li Zhong Li |
| author_facet | Xinyi Li Zhong Li |
| author_sort | Xinyi Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa094be64b67459192d82e8178c5aed8 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-fa094be64b67459192d82e8178c5aed82025-02-12T05:31:08ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-04-0158102223Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case studyXinyi Li0Zhong Li1Department of Civil Engineering, McMaster University, Hamilton, Ontario, CanadaCorresponding author.; Department of Civil Engineering, McMaster University, Hamilton, Ontario, CanadaStudy 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.http://www.sciencedirect.com/science/article/pii/S2214581825000473PrecipitationBias correctionCMIP6Quantile delta mappingScaled distribution mappingCanada |
| spellingShingle | Xinyi Li Zhong Li Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study Journal of Hydrology: Regional Studies Precipitation Bias correction CMIP6 Quantile delta mapping Scaled distribution mapping Canada |
| title | Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study |
| title_full | Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study |
| title_fullStr | Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study |
| title_full_unstemmed | Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study |
| title_short | Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study |
| title_sort | assessment of bias correction methods for high resolution daily precipitation projections with cmip6 models a canadian case study |
| topic | Precipitation Bias correction CMIP6 Quantile delta mapping Scaled distribution mapping Canada |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825000473 |
| work_keys_str_mv | AT xinyili assessmentofbiascorrectionmethodsforhighresolutiondailyprecipitationprojectionswithcmip6modelsacanadiancasestudy AT zhongli assessmentofbiascorrectionmethodsforhighresolutiondailyprecipitationprojectionswithcmip6modelsacanadiancasestudy |