Seasonal forecasts are central to good risk management in agriculture and other weather-sensitive industries as they enable informed planning and decision making well in advance of undertaking key activities. Skillful seasonal forecasts can be used to maximise benefit in good years as well as avoid losses in bad years. A basic question in forecasting is whether one prediction system is more skillful than another. This study assesses the ability of six global dynamical climate models (SEAS5 from ECMWF, System 5 from Meteo France, GloSea5-GC2 from UK Met Office, GCFS2.0 from DWD, SPSv3 from CMCC, and CFSv2 from NCEP) to forecast monthly rainfall in Australia. In order to validate these models, the AWAP rainfall is used. This analysis focuses on each calendar month with outlooks assessed for a 1-month up to 6-month lead time and investigates whether the forecast methods have better skill than climatology. As the El Nino-Southern Oscillation (ENSO) is considered an important source of skill across the globe, this study also examines possible changes in the performance of these models in seasonal rainfall forecasting over Australia in years with a strong ENSO signal. With the aim of making these models useful from the user’s perspective, models with the best skill under certain conditions and regions are highlighted.