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Based on a comprehensive tourism forecasting competition using Portugal's regional data, we study the impact of COVID-19 on the ability of time series models to forecast tourism demand. We find that the stable seasonal patterns observed before the pandemic did not persist in 2020, but regions with higher weights of domestic tourism showed much lower tourism declines and seasonal shifts. Although this change was temporary, it caused significant forecast breakdowns in all methods. However, the intensity of the break differed across models leading to important changes in model rankings, especially in the most affected regions. We discuss the effectiveness and implications of applying straightforward data adjustments and how they can attenuate the pandemic impact on ex-post assessment of tourism forecasts.