Key points are not available for this paper at this time.
Abstract. Emulators of Earth System Models (ESMs) are statistical models that approximate selected outputs of ESMs. Owing to their runtime-efficiency, emulators are especially useful whenever vast amounts of data are required, for example, for thoroughly exploring the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. This paper introduces an emulation framework that allows to emulate spatially explicit monthly mean precipitation fields using spatially explicit monthly mean temperature fields as forcing. The emulator is designed as an additional module within the MESMER(-M) emulation framework and its core relies on the concepts of Generalised Linear Models (GLMs). Precipitation at each (land-)grid point and for each month is approximated as a multiplicative model with two factors. The first factor entails the temperature-driven precipitation response and is assumed to follow a Gamma distribution with a logarithmic link function. The second factor is the residual variability of the precipitation field. The residual variability is assumed to be independent of temperature, but may still possess spatial precipitation correlations. Therefore, the monthly residual field is decomposed into independent Principal Components and subsequently approximated and sampled using a Kernel Density Estimation with a Gaussian kernel. The emulation framework is tested and validated using 24 ESMs from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). For each ESM, we train on a single ensemble member across scenarios and evaluate the emulator performance using simulations with historical and SSP5-8.5 forcing. We show that the framework captures grid point specific precipitation characteristics, such as variability, trend and temporal auto-correlations. In addition, we find that emulated spatial (cross-variable) characteristics are consistent with that of ESMs. The framework is also able to capture compound hot-dry and cold-wet extremes, although it systematically underestimates their occurrence probabilities. The emulation of spatially explicit, coherent monthly temperature and precipitation timeseries is a major step towards the representation of impact-relevant variables of the climate system in a computational efficient manner.
Schöngart et al. (Mon,) studied this question.