We propose the lag 1 autocorrelation of daily precipitation as a simple
diagnostic of tropical precipitation in climate models. This metric
generally has a relatively uniform distribution of positive values over
the tropics. However, selected land regions are characterized by
exceptionally low autocorrelation values. Low values correspond to the
dominance of high-frequency variance in precipitation. Consistent with
previous work, we show that CMIP6 climate models overestimate the
autocorrelation. Global kilometer-scale models capture the observed
autocorrelation pattern when deep convection is explicitly simulated.
When a deep convection parameterization is used, the autocorrelation
increases across the tropics, suggesting that land surface-atmosphere
interactions are not responsible for the changes in precipitation
variability. Furthermore, an accurate simulation of convectively coupled
equatorial waves does not necessarily lead to a correct representation
of the autocorrelation, and vice versa. This suggests other driving
processes for the autocorrelation pattern.