Multi-scale interactions between the main players of the atmospheric water cycle are poorly understood, even in present-day climate, and represent one of the main sources of uncertainty among future climate projections. Here, we present a method to downscale observations of relative humidity available from the passive microwave sounder SAPHIR at a nominal horizontal resolution of 10 km to the finer resolution of 90 m using scattering ratio profiles from the lidar CALIPSO. With the scattering ratio profiles as covariates, an iterative approach applied to a non-parametric regression model based on Quantile 5 Random Forest is used to effectively incorporate into the predicted relative humidity structure the high-resolution variability from cloud profiles. Results are presented for tropical ice clouds over the ocean: based on the coefficient of determination (with respect to the observed relative humidity) and the Continuous Rank Probability Skill Score (with respect to the climatology), we conclude that we are able to successfully predict, at the resolution of cloud measurements, the relative humidity along the whole troposphere, yet ensuring the best possible coherence with the values observed by SAPHIR. By providing a method 10 to generate pseudo-observations of relative humidity (at high spatial resolution) from simultaneous co-located cloud profiles, this work will help revisiting some of the current key barriers in atmospheric science. A sample of co-located SAPHIR and CALIPSO observations, together with the downscaled relative humidity predictions, is made available at http://dx.