Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study
Bibliographic record
Abstract
Canada 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). 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. • Two bias correction methods are assessed for high-resolution daily precipitation. • CMIP6 GCMs and ensemble means are used to identify the optimal bias corrected model. • QDM and SDM significantly improve the performance of individual models. • SDM corrected ensemble mean shows the best performance over Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".