Picture a web stretched across every corner of the Earth: each strand is a coincidence of temperature fluctuations at two points. In ordinary years, the web is neatly divided into regional clusters, but when El Niño arrives, the strands break and reweave, linking distant continents. That is exactly what climatologists saw when they turned daily temperatures from 1948 to 2015 into a giant network of 10,242 nodes.

Each node is a point on the map, and a link between two nodes appears if their temperatures change in sync. To avoid drowning in random coincidences, the researchers kept only the 0.5% strongest links, about 260,000 strands out of more than 52 million possible pairs. Then they measured how tightly the strands clump together, how clearly the network divides into communities, and how far links reach on average.

During strong El Niño and La Niña events, the network's modularity drops sharply while transitivity rises. This means that familiar regional boundaries blur, replaced by long-range connections spanning the globe. This is especially clear for the eastern Pacific type of El Niño: it creates teleconnections with Indonesia and West Africa that the central Pacific type lacks.

Volcanoes behave differently. After the 1991 Pinatubo eruption, transitivity also jumped, but the average link length fell, pointing to a local rather than global impact. Similar but weaker traces appeared after the 1963 Agung eruption, while El Chichón in 1982 left no noticeable imprint on the network.

This network view of climate offers a new tool: if a model reproduces the observed rewiring of the network, it can be trusted more. The next step could be a quantitative check of how strongly modularity and average link length correlate with traditional indices like ONI. Perhaps a new index could be created that better distinguishes El Niño types or predicts their onset.

When the climate web shudders from a distant warm current, its strands stretch across oceans, but when a volcano awakens nearby, only the closest strands break. This contrast between the global and the local is the main lesson of the work: climate transmits signals in different ways, and now we can see it.