Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh

The occurrence of high temperature and heavy rain events during the monsoon season are a major climate risk affecting aquaculture production in Bangladesh. Despite advances seasonal forecasting, development operational tools remains challenge. This work presents forecasting approach to predict number warm days (NWD) (NHRD) tailored two locations Bangladesh (Sylhet Khulna). is based on use meteorological pond data generate linear models relationship between three-monthly rainfall statistics NWD NHRD, evaluation skill three dynamical from North American Multi-Model Ensemble (NMME) project. were used evaluate forecasts for seasons 1-month lead time: May July (MJJ), forecast generated April, August October (ASO), July. Differences observed predicting maximum (Spearman correlation, Root Mean Square Error, Bias statistics, Willmott’s Index Agreement,), addition NHRD models, which also vary target location. In general, show higher predictive than Sylhet Khulna. Among evaluated NMME CanSIPSv2 GFDL-SPEAR exhibit best performance, they similar features terms error metrics, but lower interannual standard deviation.