Abstract A defining feature of Pacific decadal ENSO-like variability is the similarity between its spatial expression in sea surface temperature (SST) and the spatial structure of interannual ENSO variability. This similarity may indicate that the decadal variability is merely a long-term average over interannual ENSO variability. In contrast, subtle differences (namely the meridionally broadened tropical SST signature and emphasized midlatitude SST anomalies for the decadal ENSO-like pattern) may indicate that fundamentally different processes are responsible for generating variability on the decadal to interdecadal time scale. The present study attempts to reconcile the subtly different spatial structures of interannual ENSO and decadal ENSO-like variability by relating the decadal pattern to various SST patterns associated with the development of the interannual ENSO cycle. First, a statistical analysis is used to reconstruct the decadal ENSO-like SST pattern as a linear combination of interannual SST patterns. It is shown that the decadal ENSO-like pattern is well reconstructed in the absence of decadal spatial information. Next, these interannual patterns are physically interpreted in relation to the interannual ENSO cycle. The analysis reveals that the decadal ENSO-like SST pattern is obtained by averaging over three SST patterns associated with ENSO precursors, the peak of an ENSO event, and ENSO “leftovers.” The study provides a plausible physical explanation for the spatial structure of ENSO-like decadal variability as an average over variations in the interannual ENSO cycle. The results indicate that the prominent spatial features of decadal ENSO-like variability are generated by physical mechanisms that operate through the interannual ENSO cycle. This does not imply, however, that decadal processes are unimportant in altering the decadal properties of ENSO. Results may provide a framework for interpreting modeled decadal ENSO-like variability and for constraining plausible mechanisms of tropical decadal variability.