Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data; inaccuracies in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate (precipitation and temperature) database for the Western Lake Erie Basin (WLEB) for the 21st century. Two statistically downscaled bias-corrected sources of climate projections (GDO and MACA) were tested for their effectiveness in simulating historic climate (1966–2005) using ground-based station data from the National Climatic Data Center (NCDC). MACA was found to have less bias than GDO and was better in simulating certain climate indices, thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, maximum one-day precipitation could vary between 120 and 650 mm across the basin, while the number of days with no precipitation could reduce by 5–15 % under the RCP 4.5 and RCP 8.5. The number of wet sequences could increase up to 9 times and the conditional probability of having a wet day followed by wet day could decrease by 25 %. The maximum and minimum daily air temperatures could increase by 2–12 % while the annual number of days for optimal corn growth could decrease by 0–10 days. The resulting climate database will be made accessible through an open-access platform.