Climate scientists have long debated where exactly the El Niño "pool" is located: the region in the equatorial Pacific that first responds to an impending weather shift. The debate isn't due to a lack of data. The ocean is measured along longitude, latitude, and depth, and each slice contains thousands of numbers. The problem is that there are too many numbers to see the pattern directly. Imagine an interpreter at a meeting trying to memorize every word the speaker says verbatim. By the end of the hour, they couldn't even paraphrase the general meaning.

The new approach works like an experienced interpreter: first, it picks out the main points of the speaker's talk, then conveys the condensed meaning without losing the details. The temperature data are decomposed into a small number of factors, essentially "themes" that describe typical spatial patterns. The model then looks at how these factors in the past relate to factors in the future. The relationship turns out to be linear and compact: instead of millions of coefficients, only a few hundred need to be estimated.

But pure compression isn't enough. The ocean has local anomalies, such as a sharp warming off the coast of Peru that doesn't fit the overall pattern. To handle these, the model adds a sparse component, similar to a proofreader who inserts rare but important remarks that the main notes missed. This hybrid approach reduced the forecast error to 0.271, compared with 0.361 for the model without the sparse part.

The spatial maps of the factors the model extracted matched known El Niño regions. This means the method doesn't just produce accurate numbers; it speaks the language of oceanography, showing where the future climate shift is taking shape. Previously, such interpretability came at the cost of cumbersome computations, but now it's combined with efficiency.

An interpreter who has learned to pick out the main points can now handle marathon negotiations. Similarly, the method is ready for neurobiological data, financial time series, and any dataset where time and space are intertwined. The debate over the El Niño pool may soon be settled: the factor map already points to the answer.