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An Artificial Neural Networks (ANNs) approach was used to reproduce theprecipitation anomalies for the rainy seasons over the south and north partsof the Northeast of Brazil (NEB) during 1982–2009 period. The seasonalhindcasts of precipitation anomalies from Climate Forecast System v2 (CFSv2)model and the observed dominant modes of anomalous Sea Surface Temperatureover the South and North Atlantic Ocean were used as explanatory variablesseparately. The reduction of dispersion between the explanatory anddependent variables after the fit of the networks suggest the ANN as animportant complementary technique for the climate studies over the NEB.However, a large dataset are required to the models capture the non-linearprocess in more details. The practical implication of the results is thatANNs constructed here could be applied in further analyses, for example, toexplore the ANN's ability in improving the seasonal climate forecastsconsidering that the numerical and statistical methods must be complementarytools

Comparison results for the CFSv2 hindcasts and statistical downscaling over the northeast of Brazil