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Abstract. Downscaling global climate models (GCMs) provides crucial high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g. bias, spatial pattern, annual cycle and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured, i.e. the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests of model independence, data availability and future climate change spread are conducted, resulting in a final subset of two independent models that align with our a priori expectations for downscaling over the Coordinated Regional Climate Downscaling Experiment –Southeast Asia (CORDEX-SEA).
Nguyen et al. (Mon,) studied this question.