Spoofing attacks against automatic speaker verification (ASV) are increasingly serious, as deep learning enables high-fidelity speech synthesis and realistic replay attacks. We construct J-SPAW2, a Japanese corpus for evaluating ASV and antispoofing countermeasures (CM) under challenging conditions. J-SPAW2 extends J-SPAW in two directions. For physical access (PA) attacks, spoofed speech was re-recorded under varied conditions, including playback device, distance, and volume. CM models degrade in low-volume, far-field settings, whereas close-range attacks are more likely to bypass ASV. t-DCF analysis highlights this critical vulnerability. For logical access (LA) attacks, deepfake speech is generated from noisy, non-consensually recorded speech using zero-shot TTS. CM and ASV evaluations confirm that the synthesized speech bypasses existing security systems. J-SPAW2 is designed to broaden benchmark diversity by covering realistic and varied PA and LA threat conditions, thereby supporting research on robust speaker verification and anti-spoofing.