Biodiversity patterns are rapidly changing with increasing rates of colonization and extirpation associated with anthropogenic climate change. Some species are undergoing range shifts and altering their phenology to track suitable environmental conditions, while others alter their behavior to avoid unsuitable conditions or rapidly evolve their thermal or hydric tolerances. Meanwhile, many species are experiencing steep population declines as habitat becomes unsuitable whereas others are expanding their distributions as newly suitable habitat is created or as humans assist their dispersal. Researchers are increasingly integrating a growing assortment of participatory science databases with biodiversity records, high-resolution environmental information including remote sensing products, and machine learning or AI to understand these changes. Traditionally, researchers have explored relationships between climate means (via data from weather stations, remote sensing platforms, or modeled projections) and shifting biodiversity patterns under climate change. However, extreme weather events are increasing in frequency, duration, and intensity and are now becoming well appreciated for their role in pushing organisms beyond their physiological thermal or hydric tolerances, limiting where they can persist and influencing biodiversity patterns. As microclimate information increasingly becomes available through advanced modeling approaches and the widespread deployment of in situ sensors, researchers can estimate whether species might persist despite local changes in macroclimate. As climate change accelerates, researchers must employ cutting-edge datasets and techniques to generate the clearest picture of current and future biodiversity patterns and improve conservation outcomes.