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1. Introduction
Extreme precipitation events are of considerable interest because they can have major impacts on the environment, society, and economy (Sugiyama et al. 2010). According to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the frequency, intensity, spatial extent, duration, and timing of weather and climate extremes are significantly influenced by climate change (Jiang et al. 2015). Severe and extreme precipitation events are more prone to occur with the warming climate (Min et al. 2011; Rajczak et al. 2013; Stott 2016), and thus the potential changes in extreme rainfall events with climate change have become increasingly important (Hallegatte et al. 2013).
Currently, climate models are the primary tools available for making projections of future mean climate and extreme events (O’Gorman and Schneider 2009; Sillmann et al. 2013; Madsen et al. 2014; Jiang et al. 2015; Bao et al. 2017). To be confident in the projected future change in extreme precipitation events, it is essential to understand the model’s capability in reproducing historical extreme rainfall events. This capability has been examined by numerous studies, and it was shown that large uncertainty still exists in the state-of-the-art general circulation models (GCMs; Stephens et al. 2010; Min et al. 2011; Sillmann et al. 2017). Xu et al. (2011) examined the performance of models from the World Climate Research Programme’s (WCRP) phase 3 of the Coupled Model Intercomparison Project (CMIP3) and showed that these models can reproduce the observed spatial distribution of extreme precipitation in the last half of the twentieth century, but the observed interannual variations in extreme precipitation events are not well simulated. By analyzing archives from phase 5 of the CMIP (CMIP5), Rosa and Collins (2013) showed that GCMs underestimate the incidence of heavy rainfall events but overestimate the persistence of heavy precipitation. Jiang et al. (2015) quantitatively assessed the CMIP5 models in simulating precipitation extremes and showed that the performance of the models is quite different between western and eastern China, with wet biases in the west and dry biases in the east. Kopparla et al. (2013) compared the simulations of daily extreme precipitation events between high-resolution CAM4 (~0.25°) and the same model at lower resolutions (~1° and 2°). They concluded that extreme precipitation is more accurately simulated at...