The occurrence of sea surface temperature (SST) extremes may provoke profound impacts on ocean health. In the last decade, much effort has been dedicated to understanding and systematically describing marine heatwaves (MHWs) and cold spells (MCSs), defined as prolonged periods of anomalously warm or cold SSTs at a given location, respectively. However, an objective and agreed detection criterion for such extremes, applicable to past and future climates alike and separating the effects of climate variability and change, is still missing. Analysis of four decades of global daily satellite-based SST data show that the identification of extremes is strongly dependent on the chosen data set and algorithmic choices that are difficult to set objectively. Sensitivity to the reference period (baseline) occurs because the warming trend shifts SST anomalies away from the baseline, resulting in a global increase in MHWs (moderate and strong) and a decrease in MCSs (strong and extreme). Here we show that the contributions from climate change can be effectively isolated by applying a data-driven approach based on the singular spectrum analysis, which, unlike linear detrending, does not assume a prescribed behavior for the trend. Detrending removes long-term changes in the occurrence of SST extremes, and also affects the metrics (such as intensity and duration) widely used to characterize these events. Data set intercomparison reveals possibly spurious MHW events in the early 1980s and quantitative discrepancies in the representation of long-term variability. © 2025 Elsevier B.V., All rights reserved.

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