The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ(18)O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ(18)O are highly correlated and thus the EVI is a good predictor of precipitated δ(18)O. We then test the predictability of our EVI-δ(18)O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ(18)O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ(18)O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape.