  Studying the impact of climate change on precipitation is constrained by
finding a way to evaluate the evolution of precipitation variability over time.
Classical approaches (feature-based) have shown their limitations for this
issue due to the intermittent and irregular nature of precipitation. In this
study, we present a novel variant of the Dynamic time warping method
quantifying the dissimilarity between two rainfall time series based on shapes
comparisons, for clustering annual time series recorded at daily scale. This
shape based approach considers the whole information (variability, trends and
intermittency). We further labeled each cluster using a feature-based approach.
While testing the proposed approach on the time series of Paris Montsouris, we
found that the precipitation variability increased over the years in Paris
area.
