The dendritic structure of river networks is commonly argued against use of species atlas data for modeling freshwater species distributions, but little has been done to test the potential of grid-based data in predictive species mapping. Using four different niche-based models and three different climate change projections for the middle of the 21st century merged pairwise as well as within a consensus modeling framework, we studied the variability in current and future distribution patterns of 38 freshwater fish species across Germany. We used grid-based (11×11 km) fish distribution maps and numerous climatic, topographic, hydromorphologic, and anthropogenic factors derived from environmental maps at a finer scale resolution (250 m-1 km). Apart from the explicit predictor selection, our modeling framework included uncertainty estimation for all phases of the modeling process. We found that the predictive performance of some niche-based models is excellent independent of the predictor data set used, emphasizing the importance of a well-grounded predictor selection process. Though important, climate was not a primary key factor for any of the studied fish species groups, in contrast to substrate preferences, hierarchical river structure, and topography. Generally, distribution ranges of cold-water and warm-water species are expected to change significantly in the future; however, the extent of changes is highly uncertain. Finally, we show that the mismatch between the current and future ranges of climatic variables of more than 90% is the most limiting factor regarding reliability of our future estimates. Our study highlighted the underestimated potential of grid cell information in biogeographical modeling of freshwater species and provides a comprehensive modeling framework for predictive mapping of species distributions and evaluation of the associated uncertainties.