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Lowest events in Lake Titicaca’s water level (LTWL) significantly impact local ecosystems and the drinking water supply in Peru and Bolivia. However, the hydroclimatic mechanisms driving extreme lake-level lowstands remain poorly understood. To investigate these low lake-level events, we analyzed detrended monthly LTWL anomalies, sea surface temperature (SST) datasets covering the period 1921–2023. ERA5 reanalysis covers the period 1940–2023. A multiple linear regression model was developed to compute detrended LTWL anomalies, excluding multidecadal and residual components. Interdecadal Pacific Oscillation (IPO) and Pacific Decadal Oscillation (PDO) indices were also analyzed for the same period. Results indicate that 25% of all LTWL minima events have a short duration of <5 months, while the remaining 75% of all events have a long duration of more than 9 months, respectively. All long-lived LTWL minima events are associated with reduced moisture flow from the Amazon basin toward Lake Titicaca, but the large-scale forcing varies with the phase change of the decadal component in the 11–15 years band of the PDO (PDO_{11–15 years}). Under warm PDO_{11–15 years}phases, LTWL minima are driven by an enhanced South American low-level jet (SALLJ) caused by warm SST anomalies over the eastern Pacific Ocean. Warm SST anomalies over tropical North Atlantic and central Pacific cold events, which reinforce the cold PDO_{11–15 years}phases, driving long-lived LTWL minima through the reduction of SALLJ. Conversely, long-lived LTWL minima events under neutral PDO_{11–15 years}phases are caused by westerly flow anomalies confined to the Peruvian Altiplano. Therefore, PDO and IPO do not drive long-lived LTWL minima events because their relationship does not remain consistent over time. In conclusion, long-lived LTWL minima events exhibit a regional nature and are not driven by the PDO or IPO, as LTWL shows no consistent relationship with these decadal SST modes over time. 

Abstract Global and regional impacts of El Niño-Southern Oscillation (ENSO) are sensitive to the details of the pattern of anomalous ocean warming and cooling, such as the contrasts between the eastern and central Pacific. However, skillful prediction of such ENSO diversity remains a challenge even a few months in advance. Here, we present an experimental forecast with a deep learning model (IGP-UHM AI model v1.0) for theE(eastern Pacific) andC(central Pacific) ENSO diversity indices, specialized on the onset of strong eastern Pacific El Niño events by including a classification output. We find that higher ENSO nonlinearity is associated with better skill, with potential implications for ENSO predictability in a warming climate. When initialized in May 2023, our model predicts the persistence of El Niño conditions in the eastern Pacific into 2024, but with decreasing strength, similar to 2015–2016 but much weaker than 1997–1998. In contrast to the more typical El Niño development in 1997 and 2015, in addition to the ongoing eastern Pacific warming, an eXplainable Artificial Intelligence analysis for 2023 identifies weak warm surface, increased sea level and westerly wind anomalies in the western Pacific as precursors, countered by warm surface and southerly wind anomalies in the northern Atlantic.