Statistical Downscaling Using Kernel Quantile Regression to Predict Extreme Rainfall

Annisa Eki Mulyati, Aji Hamim Wigena, Anik Djuraidah


Rainfall is one of climate elements with diverse intensity. In extreme circumtances it is necessary to study extreme rainfall to minimize impacts that may occur. Statistical downscaling is a method that can be used to predict rainfall, which is utilizing Global Circurlation Model (GCM) output data. The characteristics of GCM output data is curse of dimensionality which causes multicollinearity. Kernel trick is one method that can be used to overcome this problem by transforming GCM output data into a high-dimensional feature space. The transformation results are modeled with kernel quantile regression. This paper presents the use of kernel quantile regression to predict extreme rainfall, compared to kernel quantile regression with principal components. The result showed that based on the RMSEP values and the correlations, both models gave relatively similiar prediction.


curse of dimensionality; kernel trick; quantile regression; rainfall; statistical downscaling

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