Abstract The record‐breaking global mean surface temperature (GMST) in 2023 and 2024 came as a surprise to the scientific community, raising the question whether it provides evidence for a recent abrupt increase in the forced global warming rate. Here, we provide a new statistical learning‐based method to quantify the forced and internal variability contributions to annual GMST based on CMIP6‐simulated surface temperatures, producing a variability‐adjusted GMST time series. We find a variability contribution to 2023 GMST of 0.1 K, with strong contributions from the El Niño Southern Oscillation region and North Atlantic. More than half of the 2022–2023 jump in temperature is explained by variability, largely owing to anomalously cool conditions in 2022. We find insufficient evidence of an abrupt increase in forced warming rate in recent years. Our results highlight the importance of variability originating outside the tropical Pacific and the need to filter out unforced variability when assessing changes in long‐term warming rates.

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