Abstract
In this study, the performances of 31 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) were evaluated using observed temperature and precipitation data sets for mainland Southeast Asia (MSA). In addition, the technique for order preference by similarity to ideal solution model (TOPSIS), which is an effective multi‐objective decision method and has been widely applied in systems engineering, was used to comprehensively evaluate the GCMs on a regional scale. The meteorological variables employed included monthly mean, maximum, and minimum air temperature series and annual/seasonal precipitation during the period 1961–2004. Results showed the overall C i values ranged from 0.56 to 0.80. Overall, ACCESS1.0, HadGEM2‐A, HadGEM2‐CC, and CESM1 (BGC) performed better than the other models with respect to air temperature and precipitation over MSA. For meteorological variables, the C i of extreme temperature by GCMs was 0.65 and higher than mean temperature at 0.61. The performance of GCMs in simulating precipitation during wet season was superior to that for annual and dry season precipitation. With respect to evaluation indicators, most of the GCMs assessed in this study failed to capture observed mean annual air temperature and annual precipitation (they underestimated annual air temperature and overestimated mean annual precipitation). When evaluating the performance of the GCMs with respect to reproducing individual climatic variables, the use of various statistical evaluation criteria provided inconsistent results. These results demonstrate the need to consider the influence of different statistical eigenvalues and to employ multiple indicators when possible to enable a comprehensive evaluation of GCMs. The results of this study provide useful information for climate change, water resource, and agricultural management research in MSA.
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