Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes

The severity and frequency of climate extremes will change in the future owing to global warming. This can severely impact the natural environment. Therefore, it is common practice to project climate extremes with a global climate model (GCM) in order to quantify and manage the associated risks. Several studies have demonstrated that a multi-model ensemble approach increases the reliability of predictions by exploiting the strengths and discounting the weaknesses of each climate simulator. However, the available multi-model averaging approaches exhibit significant drawbacks as they are not capable of extracting different climate extreme characteristics from the climate models. This study proposes a new approach that combines multiple models for projecting climate extremes by accounting for different extreme indices in the climate model performance weighting scheme. The capability of this method was evaluated with respect to reliability ensemble averaging (REA) and Taylor diagram-based GCM skill approaches for reproducing wet and dry precipitation events. The proposed multi-model averaging approach outperformed the available approaches in reducing the root mean square error (RMSE) by 5% and 54% in the wet and dry precipitation conditions, respectively. Therefore, it can be concluded that incorporating the different precipitation extremes in a multi-model combination approach could enhance the assessment of climate change impacts on the climate extremes. The climate change impacts on the extreme events, based on the proposed multi-model ensembles, is thus assessed using the standardized precipitation indexes of 3 month, 6 month, and 12 month durations. In general, the results exhibited that the frequency of wet events increases, whereas that of drought events decreases.