  Gridded satellite precipitation datasets are useful in hydrological
applications as they cover large regions with high density. However, they are
not accurate in the sense that they do not agree with ground-based
measurements. An established means for improving their accuracy is to correct
them by adopting machine learning algorithms. This correction takes the form of
a regression problem, in which the ground-based measurements have the role of
the dependent variable and the satellite data are the predictor variables,
together with topography factors (e.g., elevation). Most studies of this kind
involve a limited number of machine learning algorithms, and are conducted for
a small region and for a limited time period. Thus, the results obtained
through them are of local importance and do not provide more general guidance
and best practices. To provide results that are generalizable and to contribute
to the delivery of best practices, we here compare eight state-of-the-art
machine learning algorithms in correcting satellite precipitation data for the
entire contiguous United States and for a 15-year period. We use monthly data
from the PERSIANN (Precipitation Estimation from Remotely Sensed Information
using Artificial Neural Networks) gridded dataset, together with monthly
earth-observed precipitation data from the Global Historical Climatology
Network monthly database, version 2 (GHCNm). The results suggest that extreme
gradient boosting (XGBoost) and random forests are the most accurate in terms
of the squared error scoring function. The remaining algorithms can be ordered
as follows from the best to the worst: Bayesian regularized feed-forward neural
networks, multivariate adaptive polynomial splines (poly-MARS), gradient
boosting machines (gbm), multivariate adaptive regression splines (MARS),
feed-forward neural networks, linear regression.
