The diversity of the spatial pattern of El Niño Southern Oscillation (ENSO) events has limited our understanding of ENSO-associated predictability of regional water cycles. Various indices have been used to characterize and quantify the strength of unique facets of ENSO. A recent study (Williams & Patricola, 2018) proposed a unified approach to characterize ENSO's spatial diversity with a single non-linear index, namely the ENSO longitudinal index (ELI). It is calculated as the average longitude over the tropical Pacific where the sea surface temperature (SST) is above the threshold for deep convection (Williams & Patricola, 2018). Consequently, ELI represents the location of deep convection and the upwards branch of the Walker circulation over the tropical Pacific and tracks their zonal shifts associated with ENSO. These movements directly impact ENSO teleconnections to the mid-latitudes by modulating the extra-tropical wave-trains that impact moisture transport, storm track activity, etc. over remote regions (Patricola et al., 2020). ELI thus has been shown to be more effective at capturing teleconnections to seasonal mean and extreme precipitation regions like California and Southeastern US as compared to other conventional fixed domain indices like Niño 3.4 index (Patricola et al., 2020;Williams & Patricola, 2018).The simulation of regional precipitation and its extremes remains a challenge for Earth System Models (ESMs). Higher resolution ESMs resolve more fine scale features than prevalent low resolution (100 km in the atmosphere) models representing more realistic orographic lifting, vertical mass fluxes, coastal processes, land use as well as mesoscale ocean eddies, although still relying on parameterization for sub-grid scale processes like convection. High-resolution (HR) global models generally appear to improve the simulation of mean and extreme precipitation as compared to their low resolution counterparts, producing more intense precipitation, which can