Downscaling algorithms for CMIP6 GCM daily rainfall over India

Abstract

The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R² = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data.