The DSSAT module for Cotton Crop Modeling has been widely evaluated as a tool to predict the effect of climate change on pro- ductivity. A 2-yr multifactorial experiment was conducted at three locations of Pakistan (Faisalabad, Sahiwal, and Multan) to test and validate this model for dynamic simulation of growth, development, and seed-cotton (Gossypium hirsutum L.) yield of cultivars (3) at varying N increments (three levels) sown at two different timings (1 May and 1 June). The model was first cali- brated with field data collected during 2014 based on the best per- forming treatment (May sown and 200 kg N ha-1). Data of year 2015 was then used for further validation. Modeled values of vari- ous phenological attributes (e.g., days to anthesis and maturity) by model were reliable with recorded data, having root mean square error (RMSE) less than 2 d during both years. The RMSE values for total dry matter and seed-cotton yield were reasonably good (278–573 kg ha–1 and 237–422 kg ha–1, respectively). Applying 1980 to 2015 climate histories for the three regions, we found Faisalabad to be vulnerable up to 23.0% reduction of yield fol- lowed by Multan (14.9%), whereas the Sahiwal region is modeled as much more resilient, with less than 5% predicted reductions in yield. Finally, we found that strategic cultivar choice and timing of planting can alleviate many of the adverse impacts of changing climates on cotton yield. We conclude that the DSSAT model can be effective as a tool to make strategic cotton planting choices under changing climates.