Crop yield simulation can facilitate understanding and support policy response to climate warming. DSSAT modelling is dependable when the crop varieties characterised can manifest realistic yield-responses to weather and hence climate, in a location of interest. We strived to minimise field trial and cultivar calibration resources. Our primary objective was to identify in-built DSSAT cultivars, which when used with representations of local soils can simulate the observed maize and soybean yields of 1987–2016, across 12 counties in Southwestern Ontario. These cultivars/soils were then utilised to disaggregate historic weather contribution to trending yields, but they can otherwise serve climate projection impact studies, as envisioned. Technology had contributed to progressive time-based increases in measured yields. Hence, a mixed nRMSE and r² factor (MSF ≤ 1) enabled quick and decisive cultivar/soil selections, conditioned on closer matching simulated yield levels and variations with measured. Pio-3563 (maize) and Pio-9202 (soybean) were among the cultivars selected. Northern and southern subregional composites of simulated county yields, from final selections, compared with measured at nRMSE ≤ 0.20 and r² ≥ 0.53, the best values being nRMSE ≈ 0.08 with r² ≈ 0.80 (soybean) in the south. Modelling indicated 60% and 52% climate warming contributions to increased maize and soybean yields, respectively, across Southwestern Ontario. These percentages mostly agreed with those of other investigators using statistical methods for weather impact evaluations, which were utilised for final validation of our selections. An additional finding was that northern weather contributed 35%, while southern weather contributed 70% to increased subregional soybean yields.