The physical-chemical variability of coastal upwelling creates a mosaic of environmental conditions that affect different levels of biological organization. Understanding the mechanisms that organisms use to cope with this variability is critical for addressing the challenges that climate change imposes on coastal ecosystems. This study integrates information on transcriptomic traits, metabolic performance, and the quantity of organic biomolecules in the intertidal fish Girella laevifrons from four locations with varying upwelling intensities. The results show that fish from locations with stronger upwelling intensity have higher levels of glucose, lipids, and proteins in their muscle tissue, in addition to better physiological performance compared to fish from sites with weaker upwelling intensity. Transcriptomic analyses revealed that genes associated with multicellular development and oxygen metabolism are more highly expressed in sites with stronger upwelling intensity, whereas genes related to protein ubiquitination are more expressed in sites with weaker upwelling intensity. In response to the mosaic of upwelling intensities (SAM-SST), and in-situ temperature, nutrients and oxygen variation observed in field, fish showed differential responses, suggesting local adaptations process that maximize ecological success in these areas with different physical-chemical conditions. Future studies should consider the integration of molecular tools to better understand the responses of organisms to environmental variability as upwelling intensities. This will help elucidate the complex interactions between environmental factors and biological responses, providing insights into how marine organisms might adapt to changing conditions. Understanding these mechanisms is essential for predicting the impacts of climate change on coastal ecosystems and for developing effective conservation and management strategies. The integration of transcriptomic data with metabolic and physiological performance measures offers a comprehensive approach to studying the adaptive responses of marine organisms to their dynamic environments considering the future responses in face to predict global change.