A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.

中文翻译：

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偏差校正气候模拟的多模型集合中的加权方案

模型加权方案对于预测未来变化的多模型气候集合很重要。气候模型输出在使用之前通常需要进行偏差校正。当应用偏差校正（BC）时，通常会得出相等的模型权重，因为某些BC方法会导致观测值和历史模拟完全匹配。有时会批评这种相等的权重，因为它没有考虑模型性能。在应用BC之前，可以从原始数据中获得反映模型性能的不等权重。但是，我们已经观察到某些模型产生的权重过高，而其他所有模型产生的权重却极低。这种现象可能部分是由于某些模型更适合或校准了给定应用的观测值。为了解决这些问题，我们在这项研究中考虑了一种混合加权方案，包括相等和不相等的权重。所提出的方法在计算历史权重时对历史数据进行“不完美”校正，而在计算整体预测时将普通BC应用于未来数据。我们为BC采用分位数映射方法，并对基于性能的加权平均采用贝叶斯模型进行平均。此外，研究了基于卡方检验统计量和连续排名概率得分选择最佳校正率的技术。使用完善的模型测试与普通合奏进行比较。