Techniques to preprocess the climate projections—a review

Abstract

Model-derived climate projections have been used by decision-makers for climate change impact assessment, adaptation, and vulnerability studies at large scale. However, they are reported to have significant bias against observed data. The accuracy of dynamically downscaled data depends on the large-scale forcings; however, they still have some systematic errors, so it requires further bias correction. Before using these data for further studies, they need to be processed for performance evaluation. This review article provides current understanding in the field of analyzing global climate projections. It includes studies from the multi-criteria decision-making approaches along with its pros/cons to the performance evaluation of climate models. Moreover, this article discusses several bias correction approaches, multi-model ensemble approaches, and their applications for climate change studies.