Juan Nathaniel 

Accurate and localized forecasting of climate variables are important especially in the face of uncertainty imposed by climate change. However, the data used for prediction are either incomplete at the local level or inaccurate because the simulation models do not explicitly consider local contexts and extreme events. This paper, therefore, attempts to bridge this gap by applying tree-based machine learning algorithms to correct biases inherent in simulated, reanalysed climate model against local climate observations in differing tropical climate subsystems of Indonesia. The new observation datasets were compiled from various weather stations and agencies across the country. Our results show that regions of tropical savanna experience greatest bias corrections, followed by the tropical monsoon and tropical forest. Finally, to account for extreme events, we embed regional large-scale climate events into these models. In particular, we incorporate ENSO to account for the residual error of extreme rainfall observations, and have achieved an improved bias-correction of 36.67%.