Predictability of Chilean Coastal El Niño: Insights From A Low-Order Modeling Approach

Chile Niño is a seasonally modulated coastal warm mode that affects northern--central Chile and challenges early-warning systems because events are short-lived and geographically confined. Here we assess the predictability of the Chile Niño Index (CNI) using a hierarchy of data-driven inverse models, ranging from a baseline Linear Inverse Model to Seasonally Varying and nonlinear extensions, and a lightweight hybrid scheme in which a Long Short-Term Memory network is trained to correct systematic forecast residuals. We first show that the Linear Inverse-Model framework reproduces key characteristics of coastal sea-surface temperature variability associated with Chile Niño, supporting its suitability for predictability assessment. Deterministic and probabilistic verification identifies a clear window of forecast skill for austral-autumn initializations (April--May) at short lead times (1--3 months), together with a secondary but shorter-lived enhancement for early-winter initializations (June--July) that is largely confined to 1--2-month leads. Within these windows, the Seasonally Varying Extended Configuration yields the strongest correlations, the lowest normalized errors, and the most reliable probabilities of warm and cold coastal conditions. The Hybrid correction does not substantially increase the short-lead maximum, but it consistently improves performance at intermediate lead times (4--8 months), with the largest gains when warm eastern Pacific conditions precede the target period. A case study of the 2017 event illustrates this added value: the inverse model captures the onset of coastal warming but underestimates its magnitude and delays the phase transition, whereas the hybrid scheme better maintains warm persistence and improves the timing of the reversal. Overall, our results indicate that a seasonally explicit stochastic Inverse-Model framework, augmented with a parsimonious data-driven correction, provides a physically interpretable and computationally efficient basis for coastal warming prediction in the southeast Pacific.