Regional climate models (RCMs) include both terrestrial and atmospheric compartments and thereby allow studying land–atmosphere feedback, in particular, the impact of land-use land cover driven by biogeophysical processes on regional climate. In this study, a method is developed to separate the signals from the noise in RCM simulations of the effects of changes in land use, using perturbed initial boundary conditions (PICs). We want to know how many ensemble members are required to identify robust and statistically significant land-use land cover change (LULCC) effects from RCM LULCC studies. The method is applied to a case study of urbanization and deforestation, for which LULCC scenarios are implemented in the RCM Weather Research and Forecasting (WRF). Based on WRF ensemble simulations with PICs for 2010, the signal-to-noise ratio (SNR) is used to identify areas with pronounced effect of an LULCC or, rather, the parametrization of the land-use classes. While in the urbanization scenarios clear and statistically significant signals are found for air temperature and for both latent- and sensible heat (SNR values up to 24), the effe ... mehrcts are less pronounced for precipitation, and for deforestation in general (SNR values < 1). For the case study of urbanization and precipitation, the impact of the ensemble size is studied in order to derive robust conclusions about the effects of LULCC on precipitation. We conclude that single RCM realizations of different land-use representations are not sufficient to derive LULCC-induced signals, particularly not for precipitation. Small ensemble sizes led to concluding there were significant LULCC-induced precipitation signals, but these disappeared when the ensemble size was increased. Our regional analysis suggests the need for ensemble sizes well above 10 for precipitation.