Session-based recommender systems often use black-box models to dynamically make recommendations based on current session interactions. However, explaining why an item is recommended is required under various legislations to increase the interpretability of the system. A popular approach for explaining recommendations is additive feature attribution, which assigns a score of how much each feature contributes to the prediction, such that the sum of the scores equals the prediction score. We identify two limitations in applying additive feature attribution to interpret session-based recommendations: 1. Additive feature attribution does not model the attribution of sequential dependencies in the session interactions learned by the recommendation model; it assumes that interactions occur independently of each other. 2. As additive feature attribution relies on independent features, it fails when the features are correlated due to repeated interactions in sessions. We empirically verify the impact of these limitations upon explanation faithfulness. We further fix these limitations, by presenting a simple occlusion-based feature attribution approach that is specifically tailored to session-based recommendations. Our method computes joint feature attributions for sets of interactions with sequential dependencies and sets of repeated interactions to account for their non-linear relations. Experimental results on multiple datasets and models confirm that our method is more faithful and stable than state-of-the-art attribution-based explanation methods. Our code and additional analyses are publicly available at https://github.com/simonebbruun/explaining_session_based_RSs .