• rainfall;
  • downscaling;
  • stochastic models

[1] A realistic description of land surface/atmosphere interactions in climate and hydrologic studies requires the specification of the rainfall forcing at aggregation scales of 1 hour or less. This is in contrast with the wide availability of daily rainfall observations and with the typically coarse output resolution of climate and numerical weather forecast models. Several methods have been devised to generate hourly or subhourly data from daily or monthly values, which usually rely on statistical regressions determined under the current climate conditions. Here we present a new method for downscaling rainfall in time using theoretically based estimates of rainfall variability at the hourly scale from daily statistics. The method is validated on a wide data set representative of different rainfall regimes and produces approximately unbiased estimates of rainfall variance at the hourly scale when a power law–tailed autocorrelation is assumed for the rainfall process. We further demonstrate how the downscaling method together with a Bartlett-Lewis rainfall stochastic model may be used to generate hourly rainfall sequences that reproduce the observed small-scale variability uniquely from daily statistics. Conclusions of a somewhat general nature are also drawn on the capability of finite memory stochastic models to reproduce the observed rainfall variability at different aggregation scales.