Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario.This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature (SAT).Cooling degree days (CDD) are used as the weather index for trade.In this study,a call-option model was used as an example for calculating the price of the index.Two heat stress indices were developed to describe the severity and physical impact of heat waves.The daily Global Historical Climatology Network (GHCN-D) SAT data from 1901 to 2007 from the southern California,USA,were used.A major California heat wave that occurred 20-25 October 1965 was studied.The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1°×0.1° latitude-longitude grid.The resulting comparison indicates that (a) the interpolated data can be used as reliable proxy to price the CDD and (b) a normal distribution model cannot always be used to reliably calculate the CDD price.In conclusion,the data,models,and procedures described in this study have potential application in hedging agricultural and other risks.