Articles | Volume 22, issue 1
Research article
12 Jan 2018
Research article |  | 12 Jan 2018

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Jérémy Chardon, Benoit Hingray, and Anne-Catherine Favre

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Cited articles

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Asong, Z. E., Khaliq, M. N., and Wheater, H. S.: Projected changes in precipitation and temperature over the Canadian Prairie Provinces using the Generalized Linear Model statistical downscaling approach, J. Hydrol., 539, 429–446,, 2016.
Ben Daoud, A., Sauquet, E., Bontron, G., Obled, C., and Lang, M.: Daily quantitative precipitation forecasts based on the analogue method: improvements and application to a French large river basin, Atmos. Res., 169, Part A, 147–159,, 2016.
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Bontron, G. and Obled, C.: A probabilistic adaptation of meteorological model outputs to hydrological forecasting, Houille Blanche, 23–28, 2005.
Short summary
We present a two-stage statistical downscaling model for the probabilistic prediction of local precipitation, where the downscaling statistical link is estimated from atmospheric circulation analogs of the current prediction day. The model allows for a day-to-day adaptive and tailored downscaling. It can reveal specific predictors for peculiar and non-frequent weather configurations. This approach noticeably improves the skill of the prediction for both precipitation occurrence and quantity.