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

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