Precipitation plays an essential role in the hydrological cycle and influences multiple disciplines and applications. Accurate estimation of precipitation in time and space is crucial for supporting the decision-making process in various water resources, agriculture, climatology, and hydro-energy scenarios. Although rain gauges remain the most common and reliable instrument for obtaining an accurate high temporal resolution of precipitation measurement, point scale is limited, and the spatial variation effectiveness is unpractical in many parts of the world characterized by complex topology and low-density gauge network [1]. As a result, the conventional approach to areal precipitation is mainly based on spatial interpolations of the point based rain gauge data. However, this approach limitations in areas with a sparse gauge network.

Over the past few years, a large number of gridded precipitation products with different spatial resolutions and coverage periods have been developted [2,3]. These products can generally be categorized into five major types based on their methodology and attributes: satellite products (e.g., GPM, TRMM), radar-based products (e.g., OPERA), reanalysis datasets (e.g., ERA5, CERRA), stationbased gridding (e.g., CRU, GPCC), and hybrid products (e.g., CHIRPS, CMORPH). Notably out of the aforementioned datasets, CHIRPS is known for its fine resolution and wide coverage over time [4]. Several studies have been conducted to evaluate the performance of the CHIRPS precipitation product. However, these studies typically are based in comparisons with ground data from lowland precipitation stations [5][6][7][8][9], and only a few of them represent a long analysis for complex mountainous terrain. These constraints arise from the difficulties involved in instalation, maintaining and monitoring stations at high elevations, in mountain regions [10].

This study aims to evaluate the performance of CHIRPS precipitation estimates at a high-altitude Mediterranean forest in Central Greece compared to rain gauge observations over a forty-year period (1981-2020).

The research area is the University Forest of Pertouli, which is situated in the Central Greece's Pindus Mountain Range. The forest is managed by the University Forests Administration and Management Fund (UFAMF), which was granted to Aristotle University of Thessaloniki in 1934 for research and education purposes. It spans approximately 3,290 hectares and extends from longitude 39°32' E to 39°35' E and latitude 21°33' N to 21°38' N (Figure 1). It consists mainly of pure fir stands (Abies borisii regis), with elevations ranging from 1,100 m to 2,073 m above sea level. A detailed description of the study area can be found in Stefanidis et al. [11]. Since 1961, a meteorological station has been installed (1,180 m.a.s.l.) in the forest site and operated by UFAMF. Monthly precipitation data for the period 1980 -2020 were utilized for this study. CHIRPS, developed collaboratively by the Climate Hazards Group at the University of California, Santa Barbara (UCSB) and the US Geological Survey (USGS), is a gridded precipitation product with quasi-global coverage (50° S-50° N, 180° E-180° W). Operating at a spatial resolution of 0.05°, CHIRPS provides precipitation estimates from 1981 to near-present, leveraging input parameters such as monthly precipitation climatology (CHPClim), infrared (IR) sensors from geostationary satellites, and ground precipitation observations. Notably, CHIRPS incorporates data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA 3B42) to enhance its blending process, thereby enhancing its accuracy and reliability.

A variety of statistical metrics were used to evaluate the performance of CHIRPS estimates in reproducing precipitation, compared to gauged observations at the forest site. These metrics include correlation coefficient (CC), mean bias error (MBE), mean absolute error (MAE), root-mean-square error (RMSE) and relative bias (RBIAS). The detailed description of these indices can be found in previous studies by Stefanidis et al [12] and Alexandridis et al. [13]. Additionally, MBE, MAE and RMSE were converted to %MBE, %MAE and %RMSE by dividing their initial values with means of observed values, facilitating comparison of the model's performance between seasons [14].

The comparative presentation of the precipitation monthly values measured by rain-gauge and simulated by CHIRPS is shown in Figure 2a, whereas Figure 2b depicts the monthly averages of all data of the time period (1981-2020). In both cases the overestimation of precipitation by CHIRPS is evident. The monthly values present an expected dispersion, with a relatively low slope a (0.668) and moderate offcet b (22.024) value of the regression line y=ax+b, suggesting an overestimation of about 33% of the monthly values. The coefficient of determination R 2 (0.566), can be considered as adequatefor precipitation assessments suggesting the caution use of the dataset in ungauged watersheds. The overestimations are clearly shown in Figure 2b being (on absolute) higher in winter months and lower in summer, with respect to the uneven precipitation distribution that characterizes the Mediterranean climate [15,16]. The values of the annual precipitation of the two datasets present differences. The CHIRPS average annual precipitation is 1745 mm, overestimated by a percentage of +21%, compared to the ground station annual average (1443 mm), which represents an absolute difference of 302 mm. The average differences is even more variable from year to year as presented in the statistics of Table 1, where, especially, the correlation coefficient (CC) shows relatively low values (0.55) suggesting a high desprersion of the annual values.

On a seasonal basis, the differences between the two datasets are even more evident, suggesting that that the average CHIRPS precipitation of autumn is quite accurate compared to other seasons

12P4R"S789:!"";<R=>;>929>78"?SS@ ABCIaE"S789c:!";<R=>;>929>78"?SS@ 2% The above results are, in general, confirmed by the monthly statistics. The monthly CHIRPS averages are overestimated in almost all months (by +2% or 1mm in July to +64% or 84 mm in March), with the exception of August and September, when underestimated by -5% and -27% respectively. On a monthly basis, the CHIRPS dataset generally performs well, in May, July and August producing monthly precipitation estimates different by less than 10% on absolut compared to the measured values. Additionally, during these months the values of the examined statistics indices are best compared to the other months of the year (RMSE=45% and MAE=34% in May; RMSE=28.8 mm, MBE=1.2 mm, MAE=22.7mm and RBIAS=0.026 in July). However, the poor performance of the CHIRPS dataset during other months highly affect its performance on the seasonal level. For example, the excellent performance of CHIRPS at the July and the August precipitation, when its estimates are quite accurate (with differences less than 5%) is somehow undergraded by the datasets highly overestimation in June (+60%) resulting to less accurate summer (June to August) precipitation estimates. However, even in this case CHIRPS performed adequately for summer precipitation. On the other hand, the very close estimates of autumn CHIRPS precipitation, is produced by overestimated values in October (by +11%) and November (+13%) amplified by high underestimates in September (-27%) that resulted to quite accurate seasonal values for the autumn. This is critical, for monthly climate and hydrological assessments, and can lead to high uncertainties.

The evaluation of the CHIRPS precipitation product in a high-altitude forest of Central Greece reveals significant discrepancies between satellite estimates and rain gauge observations, characterized primarily by an overestimation of precipitation. Seasonal analysis further delineated the dataset's performance, showcasing relatively better accuracy in autumn and notable overestimations in spring and winter. These findings underscore the necessity for cautious application of CHIRPS data, particularly in ungauged mountainous regions where precipitation variability is high. Future research should focus on the performance evaluation of multi-source gridded precipitation across diverse climatic conditions, evaluating also CHIRPS at extreme weather events and for multiple long-operating meteorological stations. This study highlights the potential and limitations of using CHIRPS product for environmental research and resource management in complex forested landscapes.