Abstract
ABSTRACTBuilding statistical downscaling models often faces a large number of potential predictors from atmospheric circulation fields. The least absolute shrinkage and selection operator (LASSO) has been used to downscale monthly rainfall in summer over the Yangtze River Valley. Based on the shrinkage of coefficients of the model, LASSO can provide sparse models with many coefficients being zero. Geopotential height at 500-hPa was used as the predictor set. The results show that LASSO can reproduce the spatial pattern of anomalies of rainfall in most years. Furthermore, LASSO can reproduce the shift of the rainfall over the Yangtze River Valley in the late 1970s. The performance of the elastic net was also tested, and its grouping effect should be noted. It was also found that LASSO performs better than principal component regression.
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