Detailed knowledge on surface water distribution and itschanges is of high importance for water management and biodiversityconservation. Landsat-based assessments of surface water, such as the GlobalSurface Water (GSW) dataset developed by the European Commission JointResearch Centre (JRC), may not capture important changes in surface waterduring months with considerable cloud cover. This results in large temporalgaps in the Landsat record that prevent the accurate assessment of surface waterdynamics. Here we show that the frequent global acquisitions by the ModerateResolution Imaging Spectrometer (MODIS) sensors can compensate for thisshortcoming, and in addition allow for the examination of surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIScell, we developed a global rule-based regression model for estimating thesurface water fraction from a 500 m nadir reflectance product from MODIS(MCD43A4). The model was trained and evaluated with the GSW monthly waterhistory dataset. A high estimation accuracy (R2=0.91, RMSE =11.41 %, and MAE =6.39 %) was achieved. We then applied the algorithm to18 years of MODIS data (2000–2017) to generate a time series of surfacewater fraction maps at an 8 d interval for the Mediterranean. From these mapswe derived metrics including the mean annual maximum, the standard deviation, and the seasonality of surface water. The dynamic surface water extent estimatesfrom MODIS were compared with the results from GSW and water level datameasured in situ or by satellite altimetry, yielding similar temporalpatterns. Our dataset complements surface water products at a fine spatialresolution by adding more temporal detail, which permits the effectivemonitoring and assessment of the seasonal, inter-annual, and long-termvariability of water resources, inclusive of small water bodies.