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Studying the impact of climate change on precipitation is constrained byfinding a way to evaluate the evolution of precipitation variability over time.Classical approaches (feature-based) have shown their limitations for thisissue due to the intermittent and irregular nature of precipitation. In thisstudy, we present a novel variant of the Dynamic time warping methodquantifying the dissimilarity between two rainfall time series based on shapescomparisons, for clustering annual time series recorded at daily scale. Thisshape based approach considers the whole information (variability, trends andintermittency). We further labeled each cluster using a feature-based approach.While testing the proposed approach on the time series of Paris Montsouris, wefound that the precipitation variability increased over the years in Parisarea.

Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)