The use of stratospheric aerosols in solar geoengineering or climate engineering has been proposed as a potential strategy to lessen the effects of climate change in the future. In order to include cost estimation or derive optimal environmental policies, highly efficient calculations of climate change are generally required. However, the likely impacts of climate change vary greatly both between and within regions and individual countries and the majority of previous research has been focused on the forecast of global or local aggregate metrics such as annual mean temperature and precipitation, and some early models use solar reduction as the input instead of operations that can be implemented. In this thesis, four different models whose input is the injection rates of sulfate aerosols at several pre-defined latitudes are developed to predict the zonal annual mean climate metrics which could provide a more detailed description of the climate change. The EOF model has strong assumptions of linearity and separates the patterns of time and space, while the State-Space model is more flexible and could be divided into two steps: SO2 → AOD and AOD → tem- perature & precipitation. By doing so we can evaluate the performance of the two components separately and make targeted improvements. Then this thesis evaluates the performance when the state vector in the second step is simpli- fied to only 4 elements, bu doing which the matrix could be diagonalized and system could be decoupled. The thesis also talks about the role of variability of the system. Since each control simulation at the pre-defined latitudes has only 3 ensembles with different time series of injection rates, we plot the average of the prediction compared with the range of the truth across three ensembles to evaluate the error.