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Our goal is to determine whether novel surface water exposure indices that take malaria dispersal mechanisms into account, derived from new high\u2010resolution surface water data, can be stronger predictors of malaria prevalence compared to precipitation. One hundred eighty candidate predictors were created by combining three surface water malaria exposures from high\u2010accuracy and resolution (5\u00a0m resolution, overall accuracy 96%, Kappa Coefficient 0.89, Commission and Omission error 3% and 13%, respectively) water maps of East Africa. Through variable contribution analysis a subset of strong predictors was selected and used as input for Boosted Regression Tree models. We benchmarked the performance and Relative Contribution of this set of novel predictors to models using precipitation instead of surface water predictors, alternative lower resolution predictors, and simpler surface water predictors used in previous studies. The predictive performance of the novel indices rivaled or surpassed that of precipitation predictors. The novel indices substantially improved performance over the identical set of predictors derived from the lower resolution Joint Research Center surface water data set (+10% <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>, +17% Relative Contribution) and over the set of simpler predictors (+18% <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>, +30% Relative Contribution). Surface water derived indices can be strong predictors of malaria, if the spatial resolution is sufficiently high to detect small waterbodies and dispersal mechanisms of malaria related to surface water in human and vector water exposure assessment are incorporated.<\/jats:p>","DOI":"10.1029\/2023gh000811","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T04:16:41Z","timestamp":1697084201000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High\u2010Resolution Earth Observation Data"],"prefix":"10.1029","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4020-8597","authenticated-orcid":false,"given":"Maurice W. M. 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