Reconstruction of Zonal Precipitation From Sparse Historical Observations Using Climate Model Information and Statistical Learning

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 seasonally averaged 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 (ZMP) variability is recovered, in particular over the Northern hemisphere and in parts of the tropics. Finally, we demonstrate that the reconstructed ZMP trends are outside the variability of pre-industrial control simulations, and are largely 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.

Automated summary

Future changes in precipitation have significant impacts on societies worldwide, but uncertainties persist due to limited observational coverage, climate variability, and model disagreement. In this study, we propose a new method to reconstruct seasonally averaged zonal precipitation using climate model simulations and sparse rain-gauge data. The reconstructed precipitation shows a signal likely caused by human influence, as it exceeds the variability range of pre-industrial control simulations and is consistent with historical simulations driven by external forcing.