Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia

Climate predictions spanning 10-year periods, known as Decadal Climate Predictions (DCPs), have become an important aspect of the latest Coupled Model Intercomparison Project (CMIP6). These DCPs have the capability to capture the El Niño-Southern Oscillation (ENSO) phenomena, which affects heatwave frequency in Southeast Asia over years to decades. This research assesses the ability of six General Circulation Model (GCM) DCPs to predict surface temperature over the Southeast Asian region, using the dcpp-A hindcast as the main product. The metrics of Anomaly Correlation Coefficient (ACC) and Mean Error (ME) are employed to assess the model outputs, with 51 hindcast datasets spanning initial years from 1960 to 2010 and ERA5 reanalysis data serving as the reference. The evaluation reveals that DCP model skill varies across lead times and subregions, with no single model consistently outperforming the others. The highest correlation values are observed during the September-October-November (SON) season, and the ENSEMBLE model demonstrates the ability to increase correlation values compared to the individual DCP models. However, the ENSEMBLE approach is unable to effectively reduce ME values due to the contrasting errors among individual models. PBIAS metric aligns with the ME, consistently identifying similar areas of underestimation (mainland) and overestimation (maritime continent) across the models. Despite these challenges, the evaluation results highlight the potential of DCPs in predicting surface temperature variability for the Southeast Asian region over decadal periods, particularly in capturing ENSO-related signals. Further improvements in model initializations, internal variability representation, and bias reduction are necessary to enhance the utility of CMIP6 decadal predictions for heatwave preparedness and mitigation strategies in this vulnerable region.