Sub-seasonal to seasonal (S2S) prediction of dry and wet extremes for climate adaptation in India

Abstract

Extreme climatic events have considerable impacts on society, and their prediction is an essential tool for climate change adaptation. A reliable forecast of dry and wet extremes is crucial for developing an early warning system and decision-making in agriculture and water resources. Sub-seasonal to seasonal (S2S) forecasts can be valuable for climate adaptation in water resource and agriculture sectors due to their extended range forecast ability and accessibility of different hydrometeorological products. However, the utility of these S2S models’ forecasting capabilities is limited to a certain lead time, rendering them unsuitable for decision-making. We comprehensively examined the prediction skill of nine global S2S prediction models for precipitation and dry and wet extremes over India during the summer monsoon season (June to September). We find that ECCC, NCEP, and UKMO perform better than the other S2S models in predicting dry and wet extremes during the summer monsoon (June-September) in India. Our findings show that the better-performing S2S forecast models can be used to predict wet and dry extreme events several weeks ahead during the summer monsoon season. The extended range forecast system (ERFS), which is currently operational in India, provides better forecast skills for dry and wet extremes than most of the S2S models. However, S2S models provide an extended lead time forecast compared to ERFS. Therefore, a combination of ERFS and better-performing S2S models can be utilized in the early warning of dry and wet extremes at longer lead times.