Categorization of precipitation changes in China under 1.5°C and 3°C global warming using the bivariate joint distribution from a multi-model perspective

This study examines the changes in the intensity and frequency of precipitation in China from a multi-model perspective on 20 statistically downscaled fine-scale climate projections and categorizes them into four distinct patterns in response to globally targeted warming (1.5°C and 3°C). In a multivariate setting, the asymmetric responses of frequency and intensity to different levels of warming can be considered jointly. This study focuses on relatively moderate precipitation to determine if the ensemble of a subset of climate models, which are selected based on the categorization, can provide a better interpretation of the changing patterns compared to that from the conventional unweighted ensemble mean. The results show that the spatial distribution of the predominant category and inter-model agreement are dependent mainly on the degree of warming. As warming becomes more extensive, the projected change in precipitation tends to converge to the category that indicates an increase in both the intensity and frequency of precipitation, from the mixed-mode and even decreasing pattern. The use of subsampling to produce an ensemble of joint probability (or return period) has potential benefits in detecting asymmetric changes in the intensity and frequency of precipitation that is seen in the majority of models but hidden by the unweighted ensemble average particularly for regions where different models show mixed signals. A substantial portion of the region in China is likely to experience a transition of changes in precipitation frequency and (or) intensity under continuous warming, which would not be revealed clearly by univariate analysis.