## 1 Introduction  
Climate change (CC) refers to alterations in the climate occurring over time, driven by both natural processes and human-induced factors [1]. These changes affect various sectors, including agriculture, biodiversity, urban planning, and recreation, such as golf courses [2; 3; 4]. The increasing temperatures, erratic weather patterns, and extreme events like hurricanes and wildfires [5; 6] resulting from climate change affect ecosystems and pesticide use on golf courses [7; 8]. As global temperatures rise, golf courses experience increased pest infestations [9], leading to higher reliance on pesticides and raising environmental concerns such as water pollution and biodiversity loss [10; 11]. There is an urgent need to develop sustainable management practices to mitigate these impacts. Golf course management requires substantial effort and resources, often relying on pesticides to maintain the desired turf quality [12]. In Quebec, golf course managers have been legally required since 2003 to submit a \"presticide reduction plan\" (PRP) to the Ministry of Environment. These PRPs outline the specific quantities of pesticides used and include objectives for reducing pesticide usage over time [13]. However, weather conditions significantly influence pest occurrences, making it challenging to predict pesticide usage and set realistic reduction goals. Machine learning (ML) models have been increasingly applied in predicting pesticide usage in agriculture. Several studies have highlighted the effectiveness of ML models, such as K-nearest neighbor, random forests (RFs), and convolutional neural networks (CNNs), in optimizing pesticide application [14; 15; 16; 17]. In particular, [PERSON] et al. [16] developed a hybrid machine learning model (RF-SVM-GOA) to predict pesticide use on Quebec golf courses, showing high accuracy compared to individual methods. To address the constraints of the prior research, [PERSON] et al. [17] introduced a more advanced model combining CNN with RF. Key variables like total precipitation and average temperature are identified as the most influential factors in forecasting pesticide use. Climate change models, including the Coupled Model Intercomparison Project (CMIP) phases, are critical tools in predicting climate impacts. The CMIP5 and CMIP6 models, using Canadian Earth System Models (CanESM2 and CanESM5), provide valuable data for understanding future climate scenarios [18; 19; 20; 21; 22]. The main differences between CMIP5 and CMIP6 lie in their use of Representative Concentration Pathways (RCPs) and Socioeconomic Pathways (SSPs) for projecting climate changes [23; 10; 21]. Studies comparing CMIP5 and CMIP6 across various regions, including Canada, have demonstrated notable differences in climate projections, particularly in terms of temperature anomalies and precipitation changes [24; 25; 26; 27]. Multiple investigations have contrasted CMIP5 and CMIP6 across various geographical areas. [PERSON] et al. [24] compared climate change model projections focusing on Canada. The researchers noted that the CMIP6 ensemble projections demonstrate a slightly more pronounced scaling pattern for temperature anomalies in northern Canada. This behavior is also observed for specific indices related to extreme and moderate events. [PERSON] et al. [25] compared CMIP5 and CMIP6 models over Southeast Asia (SEA) using 13 global climate models (GCMs) from both CMIP simulations. The authors revealed that CMIP6 exhibited comparable performance to CMIP5 across most aspects, with the exception of rainfall. Rainfall witnessed a more substantial rise, whereas temperature experienced a diminished increase under CMIP6 as opposed to CMIP5. Given the findings from CMIP6, the authors advised contemplating new climate change mitigation policies explicitly tailored to the SEA region. In a study by [PERSON] et al. [26], the impacts of worldwide climate change on both the agricultural yield and hydrological cycle were explored, specifically within a heavily irrigated management context.