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We investigate the potential of utilizing the precipitation emulator MESMER-M-TP as a tool to gain insights into precipitation patterns derived from Earth System Models (ESMs). MESMER-M-TP generates spatially explicit, monthly mean precipitation fields (2.5x2.5 resolution) by employing spatially explicit, monthly mean temperatures as input. The approach involves modeling local precipitation as the response variable of a generalized linear model (GLM) with local modes of temperature variability as predictive variables.The emulator is trained on 24 different ESMs from the CMIP6 dataset based on a single ensemble member across Shared Socioeconomic Pathways (SSPs). This results in a set of 24 distinct parameter sets for each month and location. These parameters link precipitation to temperature via the GLM, providing a basis for quantitatively analyzing inter-model differences and parametric uncertainties. We focus on three key aspects: (1) Investigating parameter distributions to identify locations and months with poor inter-model agreement and understanding how individual predictors contribute to overall discrepancies. (2) Utilizing a clustering-based approach to group the 24 climate models based on their parameters, revealing consistency with genealogy and code streams of CMIP6 model development. (3) Exploring the sensitivity of the emulator to parameter choices.This explorative analysis offers valuable insights into the intricacies of precipitation modeling in ESMs by providing a quantitative understanding of inter-model variations and exploring sampling strategies that take inter-model variations into acocunt.
Schöngart et al. (Mon,) studied this question.