Maximilian Kotz, Leonie Wenz, Stefan Lange, Anders Levermann

Projections of precipitation from global climate models are crucial for risk assessment and adaptation strategies under different emission scenarios, yet model uncertainty limits their application. Here, we assess inter-model differences by separating the response of precipitation to anthropogenic forcing within 21 individual, bias-adjusted CMIP6 models using a pattern filtering technique. The forced response of mean precipitation, the number of wet days and the intensity and frequency of daily extremes are identified using low-frequency component analysis. Inter-model agreement in the sign of local change is moderate across land areas, with better agreement for extreme metrics (90\% of models agree on 51, 41, 61, 61\% of land area, for each metric respectively). Differences in the average magnitude of local changes are also large but can be explained well by the magnitude of global surface warming, despite model differences in the sign of local change (R^2 of 0.81, 0.79, 0.69, 0.79). Moreover, we show that these temperature-precipitation scaling relationships can be identified robustly within individual climate models from inter-temporal changes in the detected forced response (median R^2 of 0.82, 0.82, 0.76, 0.87). Inter-model spread in these relationships is considerable (coefficient of variation of 22, 33, 26, 17%), thus diagnosing a source of the uncertainty in the magnitude of projected precipitation change. These results suggest that despite uncertainty in the sign of regional change, the magnitude of future precipitation changes is well constrained by temperature-scaling relationships both across and within models. They may offer a new avenue to constrain the magnitude of future projections.