Rice is consumed by more people than any other grain. Globally, Vietnam is one of the largest exporters of rice, with the majority of production occurring in the tropical, low-lying Mekong River Delta. Agriculture in the Mekong River Delta is susceptible to yield losses from rising temperatures, sea level rise, and land use change as urban expansion replaces productive farmland. Most studies that assess climate change impacts to rice paddy yields are conducted at global- or continental-scales, and use general information on management practices to simulate production. Here, we use management information from farmers and published information on soils collected in Can Tho, a centrally-located province in the Mekong Delta. These data, along with projected mid-century (2040–2069) climate data for the RCP4.5 and RCP8.5 greenhouse gas emissions scenarios, are used to drive the Decision Support System for Agrotechnology Transfer (DSSAT) platform to project future rice paddy yields using the CERES-Rice model. The results indicate that yields decline for all three rice-growing seasons in Can Tho city for both emissions scenarios when CO2 fertilization is not considered (5.5–8.5% annually on average depending on the emissions scenario). Increasing irrigation and fertilizer did not offset these losses, but simulated CO2 fertilization did compensate for yield declines caused by increasing temperatures (yields were modeled to be up 23% higher when CO2 fertilization is considered). However, we caution that estimated yield gains from CO2 fertilization are optimistic, and these modeled values do not consider rises in ozone, which can diminish yields. Continued and future dam construction could negatively affect agriculture in the region, and current government policies prohibit rice paddy farmers from diversifying their livelihoods to adapt to these changes. Monitoring rice agroecosystems at a fine-scale, as this study does, is necessary to best capture the impact that varying management practices can have on local yields. When these differences are captured, future impacts of climate change can be modeled more effectively so that local policymakers can make informed decisions about how to offset yield losses and use farmland more efficiently.