Seasonal precipitation skill in current climate models is due to initial tropical conditions alone
Using the model-analog forecast technique, we demonstrate that seasonal prediction skill of global precipitation anomalies in current climate models arises entirely from the initial sea surface temperature anomalies (SSTA) within the tropics. Model-analogs generate predictions by matching initial observed conditions to climate states drawn from pre-existing libraries of long model simulations; the subsequent time evolution of the matched states in the model forms a forecast ensemble that can replicate seasonal SSTA prediction skill of traditionally-initialized dynamical models. In this study, we show that model-analogs also capture the monthly-to-seasonal precipitation skill of current forecast models for almost all regions and seasons worldwide. Model-analogs are selected by matching monthly-mean SSTA from uninitialized CESM2 large ensemble runs to observations, between 60°S-60°N, for the years 1979–2021. At one season lead, the resulting model-analog hindcasts have global monthly precipitation skill that is statistically indistinguishable from the Seasonal to Multi-Year Large Ensemble (SMYLE), traditionally-initialized hindcasts using the same model and initialization dataset. This model-analog skill is not significantly improved by including observed 500 hPa geopotential height and/or soil moisture anomalies in model-analog selection. Additionally, multi-model ensemble-mean model-analog hindcasts based only upon tropical monthly mean SSTA, drawn from 33 CMIP6 model pre-industrial control runs, show seasonal precipitation skill comparable to (and for longer leads, even somewhat higher than) that of the operational North American Multi-Model Ensemble, particularly for the U.S. Southwest and Southeast regions. That is, if significant initial sources of extratropical seasonal precipitation skill exist beyond the tropics, current models appear unable to realize them.