Nonparametric quantile mapping using the response surface method – bias correction of daily precipitation

Abstract

Currently, regional climate models are widely used to provide projections of how climate may change locally. However, they sometimes have a spatial resolution that is too coarse to provide an appropriate resolution for the local scale. In this paper, a new nonparametric quantile mapping method based on the response surface method was proposed to perform an efficient and robust bias correction. The proposed method was applied to correct the bias of the simulated precipitation for the period of 1976–2005, and the performance and uncertainty were subsequently assessed. As a result, the proposed method was effectively able to reduce the biases of the entire distribution range, and to predict new extreme precipitation. The future precipitation based on representative concentration pathways of RCP 4.5 and 8.5 were bias corrected using the proposed method, and the impacts of the climate scenarios were compared. It was found that the average annual precipitations increased compared to the past for both scenarios, and they tended to increase over time in the three studied areas. The uncertainty of future precipitation was slightly higher than in the past observation period.