Accurately projecting future precipitation patterns over land is crucial for understanding climate change and developing effective mitigation and adaptation strategies. However, projections of precipitation changes in state-of-the-art climate models still exhibit considerable uncertainty, in particular over vulnerable and populated land areas. This study aims to address this challenge by introducing a novel methodology for constraining climate model precipitation projections with causal discovery. Our approach involves a multistep procedure that integrates dimension reduction, causal network estimation, causal network evaluation, and a causal weighting scheme which is based on the historical performance (the distance of the causal network of a model to the causal network of a reanalysis dataset) and the interdependence of Coupled Model Intercomparison Project Phase 6 (CMIP6) models (the distance of the causal network of a model to the causal network of other climate models). To uncover the significant causal pathways crucial for understanding dynamical interactions in the climate models and reanalysis datasets, we estimate the time-lagged causal relationships using the Peter-Clark momentary conditional independence (PCMCI) causal discovery algorithm. In the last step, a novel causal weighting scheme is introduced, assigning weights based on the performance and interdependence of the CMIP6 models' causal networks. For the end-of-century period, 2081-2100, our method reduces the very likely ranges (5th-95th percentile) of projected precipitation changes over land between 10 % and 16 % relative to the unweighted ranges across three global warming scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The sizes of the likely ranges (17th-83rd percentile) are further reduced between 16 % and 41 %. This methodology is not limited to precipitation over land and can be applied to other climate variables, supporting better mitigation and adaptation strategies to tackle climate change.