One of the consequences of projected climatic changes is potentially increase in frequency and intensity of regional agricultural pest outbreaks. This requires new way of analytical thinking, pest management practice, and up-to-date regulation, which we call climate smart pest management. This paper provides an integrated stochastic dynamic framework to examines the use of weather and pest infestation forecasts in agricultural pest management. First, we analytically demonstrate the role of the correlation between weather and pest infestation forecast in pest management using a stochastic optimal control framework. Next, using stochastic dynamic programming we empirically simulate optimal pest management trajectory taking into account correlation between weather and pest population predictions. The empirical case study results illustrate our theoretical inferences and show that 1. Due to faster pest infestation under climate change, farmers are forced to spray earlier in the growing season so that severe cumulative future damage on the biomass is prevented. 2. Pea production profit in the Palouse area of northern Idaho and eastern Washington can be increased by 8.5% if pea aphid management accounts for potential correlation between weather and aphid forecast errors. Acknowledgement :