This project aims to investigate the impact of sea surface temperature (SST) variability and predictability on the size structure of global fish communities. The initial phase of the project involved a trial study focusing on reef fish communities in Australia. The trial highlighted the importance of statistical attributes beyond the mean, such as variance, seasonality, and the colour of noise, in predicting the community-weighted mean (CWM) and variance (CWV) of fish body size. The current project seeks to expand this analysis to a global scale, leveraging a large volume of time series data to calculate predictability metrics for SST worldwide. For this purpose, we will utilise Level 4 SST data from the Copernicus Marine Service, which provides high-resolution, quality-controlled ocean data. This analysis will expand our knowledge of the effects of climate change on marine ecosystems across the globe. In addition to this, the project will employ Bayesian statistics to predict community structure. The findings from this project will contribute significantly to our understanding of how environmental dynamics influence marine life. By identifying regions where fish communities are particularly vulnerable to climate variability, we can prioritise these areas for protection and management. This project, therefore, has significant implications for both ecological research and practical conservation strategies in the face of ongoing climate change.