Future projected changes in precipitation substantially impact societies worldwide. However, large uncertainties remain due to sparse historical observational coverage, large internal climate variability, and climate model disagreement.
Here, we present a novel reconstruction of large-scale zonal precipitation metrics from sparse rain-gauge data using regularized regression techniques that are trained across climate model simulations.
Subsequently, we test the reconstruction on independent satellite data and reanalyzed precipitation, and find a large fraction of historical zonal mean precipitation variability is recovered, in particular over the Northern hemisphere and in parts of the tropics. Finally, we demonstrate that the reconstructed zonal mean precipitation trends are outside the variability of pre-industrial control simulations, and are consistent with the range of historical simulations driven by external forcing. Overall, we illustrate a novel way of estimating seasonally-averaged zonal precipitation from gauge data, and trends therein that show a signal very likely caused by human influence.