  Merging satellite products and ground-based measurements is often required
for obtaining precipitation datasets that simultaneously cover large regions
with high density and are more accurate than pure satellite precipitation
products. Machine and statistical learning regression algorithms are regularly
utilized in this endeavour. At the same time, tree-based ensemble algorithms
are adopted in various fields for solving regression problems with high
accuracy and low computational cost. Still, information on which tree-based
ensemble algorithm to select for correcting satellite precipitation products
for the contiguous United States (US) at the daily time scale is missing from
the literature. In this study, we worked towards filling this methodological
gap by conducting an extensive comparison between three algorithms of the
category of interest, specifically between random forests, gradient boosting
machines (gbm) and extreme gradient boosting (XGBoost). We used daily data from
the PERSIANN (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE
Retrievals for GPM) gridded datasets. We also used earth-observed precipitation
data from the Global Historical Climatology Network daily (GHCNd) database. The
experiments referred to the entire contiguous US and additionally included the
application of the linear regression algorithm for benchmarking purposes. The
results suggest that XGBoost is the best-performing tree-based ensemble
algorithm among those compared...
