LAUSR

The current drought plaguing the Southwest US (SWUS) underscores the need for long-term precipitation predictability to inform sustainable planning of future ecological and economic systems. Precipitation predictability requires understanding the teleconnections and intercorrelations of a suite of climate indices that are known to impact the SWUS. However, decision criteria about the selection of El Niño and southern oscillation (ENSO) and non-ENSO indices, definition of winter months, geographical extent, temporal scale, computation of what constitutes a long-term mean, and determination of the study period, have not been systematically examined, yet have important consequences on the appropriate characterization of SWUS winter precipitation predictability. Here, we used Pearson’s correlations, Mann–Kendall tests, descriptive statistics, and principal component analyses to explore the statistical relationships between natural modes of climate variability and observed SWUS precipitation. We found no statistically significant persistent changes in the patterns of precipitation for a suite of SWUS geographical designations. Our results show that the choice of the temporal scale has an important impact on the determination of the strength of the climate signal. We show that ENSO indices were the primary determinants of SWUS precipitation, although inconsistencies persisted depending on the choice of ENSO index, the selection of SWUS geographical designation, and the chosen winter month combination. Non-ENSO indices in isolation were found inadequate to explain SWUS precipitation outcomes. Our analysis also indicates the predictability of SWUS precipitation must consider neutral ENSO events when non-ENSO modes are found to play an important role. We recommend the undertaking of a coordinated multi-decadal suite of numerical modeling experiments that systematically account for the individual and total impacts of this critical set of climate indices to improve understanding of past precipitation outcomes and by extension, improve predictability for a future for which tens of millions of people will require advanced planning.