Author(s): Satoshi Watanabe; Ayane Takenaka
Linked Author(s):
Keywords: Climate change; Climate big data; Meteorological drought; d4PDF; Extreme event; Uncertainty
Abstract: This study demonstrates the use of climate-projection big data for future water-cycle assessment by conducting a risk evaluation of meteorological drought. The analysis reveals that variability associated with prescribed sea-surface temperature (SST) patterns is the primary determinant of projection uncertainty in drought-risk estimates. These results underscore the importance of climate-projection big data—especially large, repeated experiments under fixed warming levels—for robustly characterizing extreme drought events and their uncertainty ranges, and they highlight the need to account for SST-related variability in future water-cycle risk assessments.
Year: 2026