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. Spatial downscaling of rainfall fields is a challenging mathematical problem for which many different types of methods have been proposed. Onepopular solution consists of redistributing rainfall amounts over smaller and smaller scales by means of a discrete multiplicative random cascade(DMRCs). This works well for slowly varying homogeneous rainfall fields but often fails in the presence of intermittency (i.e., large amounts ofzero rainfall values). The most common workaround in this case is to use two separate cascade models, namely one for the occurrence and another for theintensity. In this paper, a new and simpler approach based on the notion of equal-volume areas (EVAs) is proposed. Unlike classical cascades whererainfall amounts are redistributed over grid cells of equal size, the EVA cascade splits grid cells into areas of different sizes, with each of themcontaining exactly half of the original amount of water. The relative areas of the subgrid cells are determined by drawing random values froma logit-normal cascade generator model with scale and intensity-dependent standard deviation (SD). The process ends when the amount of water in eachsubgrid cell is smaller than a fixed-bucket capacity, at which point the output of the cascade can be resampled over a regular Cartesian mesh. Thepresent paper describes the implementation of the EVA cascade model and gives some first results for 100 selected events in theNetherlands. Performance is assessed by comparing the outputs of the EVA model to bilinear interpolation and to a classical DMRC model based onfixed grid cell sizes. Results show that, on average, the EVA cascade outperforms the classical method, producing fields with more realisticdistributions, small-scale extremes and spatial structures. Improvements are mostly credited to the higher robustness of the EVA model in thepresence of intermittency and to the lower variance of its generator. However, both approaches have their advantages and weaknesses. For example,while the classical cascade tends to overestimate small-scale variability and extremes, the EVA model tends to produce fields that are slightly toosmooth and block shaped compared to the observations. The complementary nature of the two approaches, and the fact that they produce errors of oppositesigns, opens up new possibilities for quality control and bias corrections of downscaled fields.

A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields