## 2 Materials and Methods
### Climate Change Models  
To project temperature and precipitation patterns up to 2100, the outcomes from the second and fifth generations of the Canadian Earth System Model versions (CanESM2 and CanESM5) were employed. CanESM2 contributes to CMIP5, while CanESM5 is a part of CMIP6. The second iteration of the Earth System Model, CanESM2 [30; 31; 32], represents the fourth generation of the coupled global climate model developed by the Canadian Centre for Climate Modelling and Analysis (CCCma) within Environment and Climate Change Canada. CanESM2's contribution was significant as part of the Canadian modeling community's involvement in the IPCC Fifth Assessment Report (AR5). CanESM5, the latest version of the model, stands as an enhanced iteration of CanESM2, designed for CMIP6, as detailed by [PERSON] et al. [33].  
Global climate models currently exhibit spatial resolutions spanning about 100 to 300 km, which are generally coarse and lack the ability to capture intricate regional nuances influencing nearby weather patterns. To address this limitation, both statistical and dynamic downscaling techniques come into play [34]. These methodologies offer localized climate data with finer details while maintaining the border climate trends derived from global models. This study employs statistical downscaling, a widely recognized approach in climate change research [7; 35; 36; 37; 38]. Statistical downscaling methods encompass the utilization of empirical statistical frameworks to describe the interconnections between extensive-scale climatic data from global models and localized effects, such as the variations in temperature or rainfall at a specific site. Statistical downscaling carries key benefits, primarily rooted in aligning statistical models with observations [35]. This eradicates biases during their implementation in current climate simulations.  
Additionally, spatial extent and precision are dictated by the scope and density of the observation network, as opposed to computational capacity, as seen in global and regional climate models [36]. It is important to note that the entirety of the computations pertaining to the downscaling of CanESM2 and CanEM5 in the current study were executed by Environment and Natural Resources Canada ([[https://climate-scenarios.canada.ca/?page=CanDCS6-data](https://climate-scenarios.canada.ca/?page=CanDCS6-data)]([https://climate-scenarios.canada.ca/?page=CanDCS6-data](https://climate-scenarios.canada.ca/?page=CanDCS6-data)) accessed on 30 May 2023). To analyze future pesticide usage, all projected pesticide uses are assessed across three defined periods: PP1 (2023-2048), PP2 (2049-2074), and PP3 (2075-2100). Indeed, the climate change models (CanESM2 and CanESM5) were applied from 2023 to 2100, resulting in the analysis of a total of 2,134,080 samples (78 years \(\times\) 12 months \(\times\) 2 models \(\times\) 3 scenarios \(\times\) 380 golf courses).  
It is important to note that the spatial analysis and visualization of the results were conducted using ArcGIS, enabling detailed mapping of temperature, precipitation, and pesticide usage patterns, while all calculations were performed using MATLAB 9.9.