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In the current context of climate change discussions, predictions of future scenarios of weather and climate are crucial for the generation of information of interest to the global community. Due to the atmosphere being a chaotic system, errors in predictions of future scenarios are systematically observed. Therefore, numerous techniques have been tested in order to generate more reliable predictions, and two techniques have excelled in science: dynamic downscaling, through regional models, and ensemble prediction, combining different outputs of climate models through the arithmetic average, in other words, a postprocessing of the output data species. Thus, this paper proposes a method of postprocessing outputs of regional climate models. This method consists in using the statistical tool multiple linear regression by principal components for combining different simulations obtained by dynamic downscaling with the regional climate model (RegCM4). Tests for the Amazon and Northeast region of  Brazil (South America) showed that the method provided a more realistic prediction in terms of average daily rainfall for the analyzed period prescribed, after comparing with the prediction made by set through the arithmetic averages of the simulations. This method photographed the extreme events (outlier) that the prediction by averaging failed. Data from the Tropical Rainfall Measuring Mission (TRMM) were used to evaluate the method

advances in meteorology

Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil