A new precipitation emulator (PREMU v1.0) for lower complexity models

Abstract. Precipitation is a crucial component of the global water cycle. Rainfall features 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. Here, we provide a data-calibrated precipitation emulator (PREMU), offering a convenient and computationally effective way to better estimate and represent precipitation simulated by Earth system models (ESMs) under different user-prescribed emission scenarios. We construct the relationship between global/local precipitation and modes of global gridded temperature and find that the emulator shows a good performance in predicting historically observed precipitation from GSWP3. The ESM-specific emulator also estimates well the simulated precipitation of nine ESMs and under four dissimilar future scenarios of future 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 (LCMs) such as IMOGEN (Zelazowski et al., 2018) and MESMER (Beusch et al., 2022), which themselves are calibrated against ESMs. Therefore, our precipitation emulator can be directly coupled within other LCMs, for instance improving on the current simpler linear scaling of precipitation changes against global warming 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. This flexibility makes LCMs an especially valuable tool for policymakers.