The seismic phases Pn and Sn play a crucial role in investigating the velocity and anisotropic characteristics of the uppermost mantle. However, manually annotating these phases can be time‐intensive and prone to subjective interpretation. Consequently, the use of travel‐time data for these seismic phases remains limited. Despite the potential of deep learning to address this challenge, the scarcity of extensive training data sets for Pn and Sn presents significant constraints. To address this challenge, our research compiled a global million‐scale benchmark data set of Pn and Sn seismic phases, namely Seis–PnSn. The data set is derived from earthquake events with epicenter distances ranging from 1.8° to 18°. The high‐quality travel‐time data used in this study are all from the International Seismological Centre and span the period 2000 to 2019. The waveform data were sourced from data centers located in different regions of the world under the International Federation of Digital Seismograph Networks. By leveraging the unique attributes of this data set, we trained baseline models and explored the prevailing challenges in deep‐learning‐based Pn and Sn phase picking as the scope transitions from local to regional epicenter distances. Our results show that the performance of the model is considerably enhanced after training on the proposed data set. Our study is a significant complement to the data foundation for future data‐driven Pn and Sn seismic phase‐picking studies, which will contribute to enhancing our understanding of the uppermost mantle structure of Earth, for example, the seismic velocity, anisotropy, and attenuation characteristics.

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