Regression-based regionalization for bias correction of temperature and precipitation

Statistical bias correction methods are inferred relationships between inputs and outputs. The constructed functions are based on available observations, which are limited in time and space. This study investigates the ability of regression models (linear and nonlinear) to regionalize a domain by defining a minimum number of training pixels necessary to achieve a good level of bias correction performance. Linear regression is used to divide northern South America into five regions. To correct the biases of temperature and precipitation, an artificial neural network (ANN) model was trained with selected pixels within each region and then used to reproduce bias-corrected temperature and precipitation at all pixels within the delineated regions. The Community Climate System Model (CCSM) provided the climate model data. Results confirm that it is possible to identify regions in terms of physical features such as land cover, topography, and climatology over which models trained with a few pixels can correct the biases of climate variables with good accuracy over the entire domain. This approach saves computational time and reduces memory usage of using ANNs for correcting biases in climate model outputs.