Evaluation of global climate models in simulating extreme precipitation in China

Abstract

Variations in extreme precipitation can be described by various indices. In order to evaluate a climate model's ability to simulate extreme precipitation, gridded extreme precipitation indices from observations are needed. There are two ways to obtain gridded extreme precipitation indices from station-based observations: either through interpolation of station-based extreme indices (EISTA) or estimated from gridded precipitation datasets (EIGRID). In this work, we evaluated these two methods and compared observational extreme precipitation indices in China to those obtained from a set of widely used global climate models. Results show that the difference between the two methods is quite large; and in some cases it is even larger than the difference between model simulations and observed gridded EISTA. Based on the sensitivity of the indices to horizontal resolution, it was suggested that EIGRID is more appropriate for evaluating extreme indices simulated by models. Subsequently, historic simulations of extreme precipitation from 21 CMIP5 (Coupled Model Intercomparison Project Phase 5) global climate models were evaluated against two reanalysis datasets during 1961–2000. It was found that most models overestimate extreme precipitation in the mountain regions in western China and northern China and underestimate extreme precipitation in southern China. In eastern China, these models simulate mean extreme precipitation fairly well. Despite this bias, the temporal trend in extreme precipitation for western China is well captured by most models. However, in eastern China, the trend of extreme precipitation is poorly captured by most models, especially for the so-called southern flood and northern drought pattern. Overall, our results suggest that the dynamics of inter-decadal summer monsoon variability should be improved for better prediction of extreme precipitation by the global climate models.