Improving seasonal predictions of meteorological drought by conditioning on ENSO states

Abstract

Abstract Useful hindcast skill of meteorological drought, assessed with the 3-month standardized precipitation index (SPI 3 M ), has been so far limited to one lead month (time horizon of the prediction). Here, we quadruple that lead time by demonstrating useful skill up to lead month 4. To obtain useful hindcast skill of meteorological drought at these long lead times, we exploit well-known El Niño-Southern Oscillation (ENSO)–precipitation teleconnections through ENSO-state conditioning. We condition initialized seasonal SPI 3 M hindcasts, derived from the Max-Planck-Institute Earth System Model (MPI-ESM) over the period 1982–2013, on ENSO states by exploring significant agreements between two complementary analyses: hindcast skill ENSO–composites, and observed ENSO–precipitation correlations. Such conditioned hindcast skill of meteorological drought is in MPI-ESM significant and reliable for lead months 2 to 4 in equatorial South America and southern North America during these regions’ dry ENSO phases. When a region’s dry ENSO phase is present at the initialization in autumn (ASO), predictions of meteorological drought show useful hindcast skill for the upcoming winter (DJF) in the respective region. The area of this useful hindcast skill is further enlarged in both regions when the respective region’s dry ENSO phase is already present in the antecedent summer (conditioning on ENSO states in JJA). Active ENSO events constitute windows of opportunity for drought predictions that are insufficiently covered by typical predictability analyses. For these windows, we demonstrate predictive skill at unprecedented lead times with a single model whose output is not bias corrected. This contribution exemplifies the value of ENSO-state conditioning in identifying these windows of opportunity for regions that are arguably most affected by ENSO–precipitation teleconnections. During these regions’ dry ENSO phases, reliable predictive skill of meteorological drought is at long lead times particularly valuable and moves the frontier of meteorological drought predictions.