Abstract. Precipitation is a crucial component of the global water cycle. Rainfall features (e.g., strength or frequency) strongly affect societal activities and are closely associated with the functioning of terrestrial ecosystems. Hence, predicting global and gridded precipitation under different emission scenarios is an essential output of climate change research, enabling a better understanding of future interactions between land biomes and climate change. Some current lower-complexity models (LCMs) are designed to emulate precipitation in a computationally effective way. However, for precipitation in particular, they are known to have large errors due to their simpler linear scaling of precipitation changes against global warming (e.g., IMOGEN; Zelazowski et al., 2018). Here, to reduce the errors in emulating precipitation, we provide a data-calibrated precipitation emulator (PREMU), offering a convenient and computationally effective way to estimate and represent precipitation well, as simulated by different Earth system models (ESMs) and under different user-prescribed emission scenarios. We construct the relationship between global and local precipitation and modes of global gridded temperature and find that the emulator shows good performance in predicting historically observed precipitation from Global Soil Wetness Project Phase 3 (GSWP3). The ESM-specific emulator also estimates well the simulated precipitation of nine ESMs and under four dissimilar future scenarios of atmospheric greenhouse gases (GHGs). Our ESM-specific emulator also reproduced well interannual fluctuations (R=0.82–0.93, p<0.001) of global land average precipitation (GLAP) simulated by the nine ESMs, as well as their trends and spatial patterns. The default configuration of our emulator only requires gridded temperature, also available from lower-complexity models such as IMOGEN (Zelazowski et al., 2018) and MESMER (Beusch et al., 2022; Nath et al., 2022), which themselves are calibrated against ESMs. Therefore, our precipitation emulator can be directly coupled within other LCMs, improving on, for instance, the current emulations of precipitation implicit in IMOGEN. The PREMU model has the opportunity to provide the driving conditions to model well the hydrological cycle, ecological processes and their interactions with climate change. Critically, the efficiency of LCMs allows them to make projections for many more potential future trajectories in atmospheric GHG concentrations than is possible with full ESMs due to the high computational requirement of the latter. By coupling with PREMU, LCMs will have the ability to emulate gridded precipitation; thus, they can be widely coupled with hydrological models or land surface models.