The possibility of increased landslide activity as a result of climate change has often been suggested, but few studies quantify this connection. Here, we present and utilize a workflow for the first time solely using publicly available data to assess the impact of future changes in landslide dynamic conditioning factors on landslide movement. In our case we apply the workflow to three slow-moving coastal landslides near Vejle, presenting the first study of its kind on Danish landslides. We examine modelled water table depth (WTD) as a dynamic conditioning factor using the DK-HIP model (Danish Hydrological Information and Prognosis system) that simulates historic and future WTD. The data show a clear correlation with landslide movement as recorded by the interferometric synthetic aperture radar (InSAR) time series for the period from 2015 to 2019. Movement of up to 84mmyr-1 occurs during wet winter months when normalized WTD exceeds +0.5m. During dry winters, no, or very little, seasonal landslide movement is observed. The DK-HIP model predicts an increase of up to 0.7m in WTD at the study area by 2100CE under the RCP8.5 (Representative Concentration Pathway) scenario (95% confidence), which exceeds the levels this area has experienced in recent decades (mean increase of 0.2m with a standard deviation of 0.25m). This is likely to result in increased landslide activity and acceleration of movement. In a previous episode of increased landslide activity linked to extreme precipitation in the early 1980s, one of the examined landslides accelerated, causing damage to infrastructure and buildings. Our study clearly shows that these landslides are sensitive to climate change and highlights the potential of utilizing high-quality, publicly available data to address these complex scientific questions. The quality and quantity of such data are ever increasing, and so is the potential of this kind of approach.