In modern microarchitectures, machine-learning-based prefetchers use past memory requests to learn access patterns and predict memory addresses, thereby prefetching data into the cache to mitigate the processor-memory speed gap. However, they face two key challenges in capturing irregular access patterns generated by complex data structures and algorithms. One is data dispersion: the disorderliness of memory addresses makes it difficult for prefetchers to extract meaningful data features. The other is temporal and spatial complexity: existing prefetchers fail to effectively learn temporal and spatial characteristics, and thus are unable to explore more complex access patterns. To resolve these challenges, we propose TRACED, a novel temporal graph neural network-based prefetcher aimed at learning access patterns of memory addresses. TRACED consists of two key components: a dynamic clustering component and a temporal graph neural network component. In the dynamic clustering component, we introduce a similarity function to quantify the similarity of memory addresses. Based on the quantified similarity, we dynamically group unordered memory addresses into different clusters. This ensures that the memory addresses in each cluster are ordered and change smoothly, thus resolving the first challenge. The temporal graph neural network component constructs a spatiotemporal graph to represent relationships among memory addresses. This helps capture temporal and spatial characteristics both across and within clusters, thus resolving the second challenge. This article demonstrates the effectiveness of the proposed prefetcher through experiments. Specifically, in terms of accuracy, TRACED outperforms BO, SPP, DOMINO, Delta-LSTM, and VOYAGER by 2.29%–40.83% on average. Furthermore, TRACED attains remarkable coverage of 55.67% and IPC of 43.75%, outperforming all competing approaches in both metrics.