The characteristics of low flow periods, especially regarding their low temporal dynamics, suggest that estimation of metrics related to these periods may be carried out using simplified statistical model approaches that base on a rudimentary input of aggregated local meteorological information. Compared to physically-based or even strongly conceptualized hydrological models, such approaches may have the advantage of being easily set up, applicable over large study areas in a fraction of the time, and easily transferrable between regions, given that predictions are made with the accuracy required for a given purpose. In this study, simplified statistical models based on multiple linear regressions for the use in regional climate change impact analysis are proposed. Study area is the German Federal State of Lower Saxony with 28 available gauges for analysis. A number of regression approaches are evaluated. An approach using principal components of local meteorological indices as input appeared to show the best performance. This model type was eventually applied to a climate model ensemble based on the RCP8.5 scenario. Analyses in the baseline period revealed that some of the meteorological indices needed for model input could not be fully reproduced by the climate models. The predictions for the future show an overall increase in the lowest average 7-day flow (NM7Q), projected by the majority of ensemble members and for the majority of stations.