This study presents a forecast-based approach to attribute the 2021 Pacific Northwest heatwave to human-induced climate change. Using a state-of-the-art operational weather forecast model, the research demonstrates that human influence increased the likelihood of the heatwave by at least 8 [2–50] times. At the current rate of global warming, the likelihood of such extreme events doubles every 20 [10–50] years. The study shows that forecast-based attribution can combine conditional event-specific storytelling with unconditional probabilistic approaches to provide reliable estimates of human influence on extreme weather risk, which is crucial for adaptation planning. The 2021 Pacific Northwest heatwave was unprecedented in its intensity and caused many excess deaths, making it the deadliest weather event in Canada and Washington state. The heatwave was well predicted by numerical weather forecast models, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), with lead times of more than a week. The study uses a forecast-based approach to quantify human influence on the event by simulating how the heatwave might have occurred in a pre-industrial or future climate. The results show that the heatwave's intensity was reduced in pre-industrial forecasts, and the attributable warming was estimated as 1.3°C [0.7, 1.6] for the current level of anthropogenic warming. The study also finds that the relative risk of the heatwave is a factor of 8 [2, 50], indicating that the event is highly relevant for adaptation planning. The results are consistent with a linear relationship between log probabilities and global land warming, suggesting that human influence primarily affects the development of the heatwave from precursor conditions rather than the probability of those conditions. The study highlights the importance of using operational weather forecast models for attribution, as they provide reliable estimates of human influence on extreme weather events. The study also discusses the limitations of current attribution methods and suggests that further research is needed to improve the accuracy of forecasts and to account for non-linear interactions between climate system components. The use of forecast-based attribution provides a robust and practical approach to estimating changes in extreme weather risk, which is essential for climate change adaptation. The study concludes that forecast-based attribution is a valuable tool for understanding the impact of climate change on extreme weather events and for informing adaptation strategies.This study presents a forecast-based approach to attribute the 2021 Pacific Northwest heatwave to human-induced climate change. Using a state-of-the-art operational weather forecast model, the research demonstrates that human influence increased the likelihood of the heatwave by at least 8 [2–50] times. At the current rate of global warming, the likelihood of such extreme events doubles every 20 [10–50] years. The study shows that forecast-based attribution can combine conditional event-specific storytelling with unconditional probabilistic approaches to provide reliable estimates of human influence on extreme weather risk, which is crucial for adaptation planning. The 2021 Pacific Northwest heatwave was unprecedented in its intensity and caused many excess deaths, making it the deadliest weather event in Canada and Washington state. The heatwave was well predicted by numerical weather forecast models, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), with lead times of more than a week. The study uses a forecast-based approach to quantify human influence on the event by simulating how the heatwave might have occurred in a pre-industrial or future climate. The results show that the heatwave's intensity was reduced in pre-industrial forecasts, and the attributable warming was estimated as 1.3°C [0.7, 1.6] for the current level of anthropogenic warming. The study also finds that the relative risk of the heatwave is a factor of 8 [2, 50], indicating that the event is highly relevant for adaptation planning. The results are consistent with a linear relationship between log probabilities and global land warming, suggesting that human influence primarily affects the development of the heatwave from precursor conditions rather than the probability of those conditions. The study highlights the importance of using operational weather forecast models for attribution, as they provide reliable estimates of human influence on extreme weather events. The study also discusses the limitations of current attribution methods and suggests that further research is needed to improve the accuracy of forecasts and to account for non-linear interactions between climate system components. The use of forecast-based attribution provides a robust and practical approach to estimating changes in extreme weather risk, which is essential for climate change adaptation. The study concludes that forecast-based attribution is a valuable tool for understanding the impact of climate change on extreme weather events and for informing adaptation strategies.