GUST: Graph-Guided Continual Adaptation of Weather and Climate Foundation Models

Weather and climate foundation models must accommodate evolving prediction needs, yet existing adaptation typically assumes a static lifecycle in which each task is learned independently from the same pretrained checkpoint. We systematically investigate continual learning (CL) under sequential access to task data across three settings: variable CL expands atmospheric targets, regional CL extends geographical coverage, and climate-projection CL introduces new response objectives. We find that forgetting is strongly scenario-dependent. Direct sequential adaptation remains effective under compatible task shifts but severely degrades earlier capabilities when prediction objectives change. Cross-task transfer is also uneven and directional. Motivated by these findings, we propose Graph-guided Update Selection and Transfer (GUST), which stores acquired knowledge in frozen low-rank adapters and selectively composes their updates through a causal task graph guided by structured relations, learned task geometry, and online transfer feedback. GUST achieves the strongest overall acquisition-retention trade-off among CL methods. In variable CL, it obtains an average relative wRMSE of 1.102 and reduces relative forgetting from 39.55% to 9.67%. In regional CL, it achieves a relative wRMSE of 1.044 with zero measured forgetting. In climate-projection CL, it achieves the lowest oracle-normalized error on three of four metrics. These results identify when dedicated CL is necessary and provide an effective avenue for long-term evolution of weather and climate foundation models.