# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 4 Discussion
### Reliability of the Climate RPs for Developing AgcIs  
The findings reveal a satisfactory degree of agreement among the climate RPs in reproducing temperature-based indicators. The five RPs considered in this study exhibit a satisfactory level of consistency with the observations with significant correlations (LCCC) and low differences (PBias). Overall, FYRE Climate and RFHR show the best performances at reproducing all temperature-based indicators, while SCOPE Climate consistently exhibits  
Figure 7: Same as Figure 2, with the indicator WR.  
the lowest performance. Nonetheless, it is worth noting that some discrepancies among the RPs become evident when reproducing temperature-based indicators in sites with challenging topography (e.g., high altitudes or complex terrain), such as for the Embrun or Clermont-FD stations. High-resolution RPs, such as RFHR, provide the best results for the temperature-based indicators for specific mountainous stations, highlighting their advantage for a more detailed representation of complex terrain and microclimates. Similarly, various studies reported the difficulties of accurately representing temperature within coarse spatial resolution RPs in areas under a complex topography [25; 31; 58].  
For precipitation-based indicators, the results show poorer performances compared to temperature-based indicators. RPs exhibit a lower level of agreement with the reference dataset and more pronounced discrepancies among themselves. Several studies have reported similar findings [22; 28], highlighting the RPs' challenges in accurately representing precipitation-based indices, regardless of their temporal scale. Additionally, the results consistently demonstrate RPs' tendency to overestimate the indicator linked to precipitation amount WR while simultaneously underestimating the one associated with precipitation occurrence DDs. This dual effect within the RPs can be primarily attributed to the presence of a wet bias coupled with a tendency to underestimate the frequency of dry days. This behavior is supported by multiple studies conducted in various regions of Europe [27; 29; 30].  
Regarding the mixed indicator (Hyl), the findings reveal similar behaviors among the RPs to that observed when reproducing precipitation-based indicators. All the scores are lower compared to those of the temperature-based indicators. Additionally, a consistent pattern of overestimating Hyl values by the RPs, resembling the case of WR, is also seen. While the importance of temperature is unquestionable here, it appears that precipitation has a more dominant influence on the performance of RPs in reproducing Hyl.  
Overall, FYRE Climate provides the best reproduction of the precipitation-based indicators. RFHR, ERA5, and ERA5-Land display similar performances in reproducing Agcls based on precipitation amount, and a slight improvement is observed with RFHR's for the representation of precipitation occurrence. Despite the well-marked accuracy of FYRE Climate in representing Agcls, some limitations are noteworthy. One key constraint is the data's temporal extent until 2012, which constrain the climate simulations beyond 2012. Additionally, only temperature and precipitation data are provided. Even though these two variables are important, they do not capture the full complexities of agro-climatic interactions.  
Considering these findings, further studies should extend their investigation to the RPs reliability in reproducing Agcls, considering a larger set of climatic variables beyond temperature and precipitation. While these two variables are fundamental, they do not fully capture the complexities of agro-climatic conditions. This expansion is essential since various climatic factors, such as humidity, wind, and solar radiation, significantly influence agricultural outcomes.