2020 Volume 16 Pages 80-85
Bias corrected climate scenarios over Japan were developed using two distinct methods, namely, the cumulative distribution function-based downscaling method (CDFDM) and Gaussian-type Scaling approach (GSA). We compared spatial distribution, monthly variation, and future trends. The seasonal distribution of bias-corrected data using CDFDM closely followed the original general circulation model (GCM) outputs. GSA overestimated the amount of precipitation by 12-18% in every season because of an unsuitable assumption on the probability distribution. We also examined the contributions of each source of the uncertainty in daily temperature and precipitation indices. For daily temperature indices, GCM selection was the main source of uncertainty in the near future (2026-2050), while different Representative Concentration Pathways (RCPs) resulted in large variability at the end of the 21st century (2076-2100). We found large uncertainty using the bias-correction (BC) methods for daily precipitation indices even in the near future. Our results indicated that BC methods are an important source of uncertainty in climate risk assessments, especially for sectors where precipitation plays a dominant role. An appropriate choice of BC, or use of different BC methods, is encouraged for local mitigation and adaptation planning in addition to the use of different GCMs and RCPs.