# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
###### Abstract  
Agro-climatic indicators (Agcls) provide a suitable tool to evaluate the implications of climate change on agriculture by simplifying plant-climate interactions. However, developing reliable Agcls requires high-quality historical climate datasets. Consequently, reanalysis products (RPs) are frequently used as a potential reference dataset for observed climate in agricultural studies. This study aims to compare five RPs (ERA5, ERA5-Land, SCOPE Climate, FYRE Climate, and RFHR) at reproducing observed Agcls over France. The RPs are evaluated against the SYNOP meteorological data over the 1996-2012 period, focusing on six Agcls specific to apple, maize, and vine crops. The findings show that RPs perform well in reproducing temperature-based Agcls, with some slight discrepancies in areas with complex topography. However, all RPs tend to overestimate precipitation amounts and to underestimate dry days, leading to a poor performance in reproducing precipitation-based Agcls. This study emphasizes the need for a thorough evaluation of the RPs in developing both temperature-based and precipitation-based Agcls, especially if findings are intended to support operational agricultural decision-making. climate reanalysis; agro-climatic indicators; crops; temperature; precipitation; France accepted: 27 January 2024  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. agricultural users, Agcls are effective in encouraging stakeholders to consider the potential impacts of climate change on their land-use systems and in adapting to this issue [13]. High-quality historical weather datasets are a crucial component for developing reliable Agcls. This implies having access to long observational meteorological variables with appropriate spatiotemporal resolution to describe local climate variability [14]. However, access to such high-quality historical weather datasets is often challenging due to the issues of missing data, temporal coverage, and spatial extent. Therefore, climate reanalysis products (RPs) are particularly attractive as they provide a comprehensive description of the observed climate as it has evolved during recent decades [15]. They combine past observations with forecast models to generate consistent time series of climate variables. Climate RPs are used in a wide range of applications including climate research, environmental monitoring and sectoral impacts assessments [16; 17; 18; 19; 20]. Even though the climate RPs provide the most complete numerical description of the climate, they inevitably contain some systematic biases (errors) from observations and imperfect forecast models, thus affecting their temporal and spatial consistency [21]. In addition, the data assimilation schemes used to adjust the weather model integration to actual observations is an additional source of uncertainty in the climate RPs. Many studies have evaluated and compared different climate RPs with observations in different parts of the world, at the global [22; 23], continental [24; 25; 26; 27], and regional [28; 29; 30; 31] scale. Such studies have been conducted for traditional climate parameters, such as temperature and precipitation [28; 29; 30; 31] and for extreme weather events [22; 23; 24; 26] from a single or a few climate RPs for climate and hydrological applications. The findings of these studies are mixed, as differences in study regions, variables, and time scales result in inconsistency in the evaluation of the climate RPs performance, thus implying that a site-specific analysis is required. To date, few studies have evaluated the suitability of climate RPs for agricultural applications [32; 33; 34], including for the development of specific crops-related Agcls [11; 12]. However, to our knowledge, there is a notable gap in the literature associated with the evaluation of the accuracy of multiple climate RPs in the development of specific Agcls for French crops.