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International audience; A weighting scheme jointly considering model performance and independence (PI-based weighting scheme) is employed to deal with multi-model ensemble of precipitation over China from 17 global climate models. Four precipitation properties including mean and extremes are used to evaluate model performance and independence. The PI-based scheme is also compared to a Rank-based weighting scheme and to the simple arithmetic mean (AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5°C and 2°C global warming targets (above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For total precipitation PRCPTOT (95 th percentile extreme precipitation R95P), the land fraction for a change larger than 10% (20%) is 22.8% (53.4%) in PI, while 13.3% (36.8%) in AM, under 2°C global warming. Most noticeable increase exists in the central and east part of western China.

The multi-model ensemble approach is generally considered as the best way to explore the advantage and to avoid the weakness of each individual model, and ultimately to achieve the best climate projection. But the design of an optimal strategy and its practical implementation still constitutes a challenge. Here we use the random forest (RF) algorithm (from the category of machine learning) to explore the information offered by the multi-model ensemble simulations within the Coupled Model Intercomparison Project Phase 6. Our objective is to achieve a more reliable climate projection (mean climate and extremes) over China. RF is furthermore compared to two other ensemble-processing strategies of different nature, one is the basic arithmetic mean (AM), and another is the linear regression across the ensemble members. Our results indicate that RF effectively enhances the capability in capturing spatial climate characteristics. Regions with complex topography, such as the Tibetan Plateau and its periphery, show the most significant improvements. RF projects less future warming but enhanced wet conditions across China. It also produces larger spatial variability and more small-scale features. The most obvious increase of precipitation is in the northern part and the periphery of the Tibetan Plateau. The projected changes in RF for strong precipitation are almost twice higher than in AM, while in the northwestern area, weaker increases of precipitation are projected by RF, which indicates larger spatial inhomogeneity of its projection.