Differences in micronaire of cotton fiber can affect grower returns, and influence textile quality. Therefore quantifying those effects that influence micronaire are important in developing management practices to optimise micronaire. This study proposes a method for predicting seasonal crop micronaire. The aim was to quantify the response of micronaire to temperature during boll filling and assess this information's ability to predict micronaire on an independent dataset. Utilising existing data from sowing time experiments in Australia that spanned three decades, linear responses of micronaire to both daily average and minimum temperatures were developed (r2 =0.68 for both). These responses coupled with an estimate of temperature during the boll filling period when the majority of bolls were undergoing fiber thickening were able to successfully predict the micronaire on an independent dataset (r2=0.42) despite no adjustment for other climate and management factors that may influence crop micronaire. The ability to predict temperature effects on micronaire will be useful to assess reasons for seasonal and regional differences in micronaire and assess opportunities to modify micronaire with changes in management practices that influence the timing of boll development.