The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies.The critical importance of realistically representing the complex interactions and hydrological feedback loops between the atmosphere and the land surface in climate models is well documented 1 . The ability to adequately simulate precipitation systems is often related to the horizontal grid scales resolved by the models 2 and their ability to account for non-linearity and the associated variability 3,4 . Also, this is highly dependent on a proper land surface representation as soil moisture acts as a control on water and energy fluxes from land surfaces to the atmosphere while humidity, temperature, wind, radiation and precipitation provides the drivers and input for the land-surface and subsurface water fluxes. In particular, on local to regional scales the models' ability to represent a wide range of complex physical processes at a very high spatial and temporal resolution is crucial for describing the spatio-temporal variability of precipitation 1,5,6 . Some improvements in modelling approaches and improved confidence in precipitation projections have been seen recently. These improvements include: (I) higher resolution spatio-temporal model scales with grid resolutions down to e.g. 1.5 km 7 , (II) improved physical schemes and parameterizations (including dynamical vegetation schemes 8 ) in climate models and the development of earth system models (ESMs) 9 , (III) better quality of forcing and evaluation data like the increased availability of high quality gridded data sets 10,11 and (IV) a continuing growth of computational power. That said even state-of-the-art regional climate models (RCMs), which operate at scales down to a few kilometres, still generally suffer from deficiencies in fully representing the hydrological feedback to the atmosphere from the land surface and sub-surface 12,13 . Excluding g...