The impacts of climate change pose significant challenges to global forest ecosystems, particularly in Mediterranean evergreen forests dominated by Aleppo pine (Pinus halepensis Mill.). Since the CLMS 10-daily Gross Dry Matter Productivity (GDMP) product represents the potential productivity and, by definition, does not account for water stress, this study aims to evaluate and improve the Gross Primary Productivity (GPP) estimation based on GDMP for these forests. To achieve this, we assessed the GDMP-derived GPP (RS-GPPst) against eddy covariance GPP data (EC-GPP) from six Aleppo pine forest stands across the Mediterranean basin, covering 30 site-years and spanning a climatic gradient from semi-arid to semi-humid conditions. Additionally, we analyzed the ecophysiological response of Aleppo pine to drought, focusing on environmental factors such as temperature and water stress (i.e., atmospheric based on vapor pressure deficit and edaphic based on soil water content) to refine the GPP model. Our results indicated that the RS-GPPst underestimates EC-GPP during cold periods. Using ERA5-Land data, we proposed a simplified approach to remove the temperature limitation factor and incorporated a soil water content factor, which significantly enhanced model accuracy, reduced uncertainty, and improved precision. The soil water-corrected GPP model achieved a Pearson correlation of r = 0.85, a negligible bias, and an RMSE of 1.1 gC m-2 d-1, providing a more accurate representation of GPP across varying climatic conditions. These findings highlight the importance of identifying and integrating key limiting environmental stressors, particularly water stress, into GPP models for water-limited ecosystems. The improved model, relying solely on remote sensing data without requiring in-situ measurements, offers a robust approach for large-scale carbon cycle monitoring.