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Tropical rainforests in Africa are one of the most under-researched regions in the world, but research in the Amazonian rainforest suggests potential vulnerability to climate change. Using the large ensemble of Atmosphere-only general circulation model (AGCM) simulations within the weather@home project, statistics of precipitation in the dry season of the Congo Basin rainforest are analysed. By validating the model simulation against observations, we could identify a good model performance for the June, July, August (JJA) dry season, but this result does need to be taken with caution as observed data are of poor quality. Additional validation methods have been used to investigate the applicability of probabilistic event attribution analysis from large model ensembles to a tropical region, in this case the Congo Basin. These methods corroborate the confidence in the model, leading us to believe the attribution result to be robust. That is, that there are no significant changes in the risk of low precipitation extremes during this dry season (JJA) precipitation in the Congo Basin. Results for the December, January, February dry season are less clear. The study highlights that attribution analysis has the potential to provide valuable scientific evidence of recent or anticipated climatological changes, especially in regions with sparse observational data and unclear projections of future changes. However, the strong influence of sea surface temperature teleconnection patterns on tropical precipitation provides more challenges in the set up of attribution studies than midlatitude rainfall. [ABSTRACT FROM AUTHOR]