Multipath transport protocols enable the concurrent use of different network paths, benefiting a fast and reliable data transmission. The scheduler of a multipath transport protocol determines how to distribute data packets over different paths. Existing multipath schedulers either conform to predefined policies or to online trained policies. The adoption of millimeter wave (mmWave) paths in 5th Generation (5G) networks and Wireless Local Area Networks (WLANs) introduces time-varying network conditions, under which the existing schedulers struggle to achieve fast and accurate adaptation. In this paper, we propose FALCON, a learning-based multipath scheduler that can adapt fast and accurately to time-varying network conditions. FALCON builds on the idea of meta-learning where offline learning is used to create a set of meta-models that represent coarse-grained network conditions, and online learning is used to bootstrap a specific model for the current fine-grained network conditions towards deriving the scheduling policy to deal with such conditions. Using trace-driven emulation experiments, we demonstrate FALCON outperforms the best state-of-the-art scheduler by up to 19.3% and 23.6% in static and mobile networks, respectively. Furthermore, we show FALCON is quite flexible to work with different types of applications such as bulk transfer and web services. Moreover, we observe FALCON has a much faster adaptation time compared to all the other learning-based schedulers, reaching almost an 8-fold speedup compared to the best of them. Finally, we have validated the emulation results in real-world settings illustrating that FALCON adapts well to the dynamicity of real networks, consistently outperforming all other schedulers.