Global and regional climate change assessments rely heavily on the general circulation model (GCM) outputs such as provided by the Coupled Model Intercomparison Project phase 5 (CMIP5). Here we evaluate the ability of 25 CMIP5 GCMs to simulate historical precipitation and temperature over central Africa and assess their future projections in the context of historical performance and intermodel and future emission scenario uncertainties. We then apply a statistical bias correction technique to the monthly climate fields and develop monthly downscaled fields for the period of 1948–2099. The bias‐corrected and downscaled data set is constructed by combining a suite of global observation and reanalysis‐based data sets, with the monthly GCM outputs for the 20th century, and 21st century projections for the medium mitigation (representative concentration pathway (RCP)45) and high emission (RCP85) scenarios. Overall, the CMIP5 models simulate temperature better than precipitation, but substantial spatial heterogeneity exists. Many models show limited skill in simulating the seasonality, spatial patterns, and magnitude of precipitation. Temperature projections by the end of the 21st century (2070–2099) show a robust warming between 2 and 4°C across models, whereas precipitation projections vary across models in the sign and magnitude of change (−9% to 27%). Projected increase in precipitation for a subset of models (single model ensemble (SME)) identified based on performance metrics and causal mechanisms are slightly higher compared to the full multimodel ensemble (MME) mean; however, temperature projections are similar between the two ensemble means. For the near‐term (2021–2050), neither the historical performance nor choice of models is related to the precipitation projections, indicating that natural variability dominated any signal. With fewer models, the “blind” MME approach will have larger uncertainties in future precipitation projections compared to projections by the SME models. We propose the latter a better approach in regions that lack quality climate observations. Our analyses also show that the choice of model and emission scenario dominate the uncertainty in precipitation projections, whereas the emission scenario dominates the temperature projections. Although our analyses are done for central Africa, the final Bias‐Corrected Spatially Downscaled data set is available for global land areas. The framework for climate change assessment and the data will be useful for a variety of climate assessment, impact, and adaptation studies.

In response to degraded water quality, federal policy makers in the US and Canada called for a 40% reduction in phosphorus (P) loads to Lake Erie, and state and provincial policy makers in the Great Lakes region set a load‐reduction target for the year 2025. Here, we configured five separate SWAT (US Department of Agriculture's Soil and Water Assessment Tool) models to assess load reduction strategies for the agriculturally dominated Maumee River watershed, the largest P source contributing to toxic algal blooms in Lake Erie. Although several potential pathways may achieve the target loads, our results show that any successful pathway will require large‐scale implementation of multiple practices. For example, one successful pathway involved targeting 50% of row cropland that has the highest P loss in the watershed with a combination of three practices: subsurface application of P fertilizers, planting cereal rye as a winter cover crop, and installing buffer strips. Achieving these levels of implementation will require local, state/provincial, and federal agencies to collaborate with the private sector to set shared implementation goals and to demand innovation and honest assessments of water quality‐related programs, policies, and partnerships.

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Climate change holds great potential to affect the Lake Erie ecosystem by altering the timing and magnitude of precipitationdriven river discharge and nutrient runoff in its highly agricultural watershed.Using the SWAThydrologic model and an ensemble of global climate models, we predicted Maumee River (Ohio) discharge during the 21 st centuryunder two Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios: RCP4.5 (mid-range, moderate reductions) and RCP8.5 (high, "business as usual"). Annual dischargewas projected to increaseunder both scenarios, both in the near-century (RCP4.5=6.5%; RCP8.5=2.0%) and latecentury (RCP4.5=9.2%; RCP8.5=15.9%), owing to increased precipitation and reduced plant stomatal conductance.Holding fertilizer application rates at baseline levels, we found that reduced winter surface runoff and increased plant phosphorus (P) uptake led to a respective decrease in annual total P (TP)runoff in the near-century (RCP4.5=-4.3%;RCP8.5=-6.6%) and by the late-century(RCP4.5=-14.6%; RCP8.5=-7.8%). Likewise, soluble reactive P (SRP)runoff was predicted to decrease under both scenarios in the near-century (RCP4.5=-0.5%; RCP8.5=-3.5%) and by the late-century (RCP4.5=-11.8%; RCP8.5=-8.6%). By contrast, when fertilizer application was modeled to increase at the same rate as plant P uptake, TP loadingincreased4.0% (0.9%) in the near-century and 9.9% (24.6%) by the late-century and SRP loading increased 10.5% (6.1%) in the near-century and 26.7% (42.0%) by the late-century under RCP4.5 (RCP8.5).Our findings suggest that changes in agricultural practices (e.g., fertilization rates) will be key determinants of Maumee Riverdischarge during the 21 st century.