In this paper, we leverage open data and open-source software to develop flexible, probabilistic monthly and seasonal (three-month) precipitation forecasts for the Pacific region. We use data from a Multi-Model Ensemble (MME), i.e. a large ensemble of state-of-the-art General Circulation Models (GCMs) and make use of recent developments in the Python open-source software ecosystem allowing the processing of large datasets on standard consumer grade laptops or desktop computers, of particular relevance in the Pacific context. The validation of the deterministic MME forecasts against reanalysis and observational products shows good performance, and confirms that an MME outperforms even the best single GCM. We show that the MME’s forecast performance is modulated by the phases and characteristics of the El Nino Southern Oscillation (ENSO), with the longitude of the maximum Sea Surface Temperature anomalies playing a major role. We suggest that these findings could be used to provide additional confidence information along with the operational MME forecasts. Validation metrics for the probability of drought conditions, alternatively defined as seasonal rainfall accumulations below the climatological 1 tercile (percentile 33) or 1st quartile (percentile 25) show that the MME forecasts are reliable enough for most of the region. We provide an example of how this probabilistic forecast information can be integrated with real-time rainfall monitoring, in order to highlight areas in the tropical Pacific region which are at risk of water stress (i.e., where rainfall has recently been in deficit and forecasts indicate a high likelihood of dry conditions to persist or worsen).