Drought monitoring is a key factor in water resources management and reducing vulnerability in susceptible areas. However, accurate monitoring across various geographical regions, especially those with high climatic diversity and severe limitations in access to long-term station data (such as southeastern Iran), has always faced serious and fundamental challenges. In this regard, global gridded databases, which are produced through the integration of remote sensing, numerical models, and measured data, are considered effective solutions for areas lacking sufficient data. Nevertheless, their accuracy varies across different climates; therefore, evaluating the performance of gridded indices against station-based ones is essential to ensure their correlation and agreement. The present study investigates the correlation and agreement between the station-based Standardized Precipitation Index (SPI) and the gridded SPIbase at timescales of 1, 3, 6, 9, and 12 months to assess the substitutability of gridded data. The station-based SPI was calculated using precipitation data from 8 meteorological stations in southeastern Iran (1986–2024). SPIbase was extracted from the Global Precipitation Climatology Centre data for the same period. The evaluation of consistency and agreement between the two indices was carried out using Pearson correlation, weighted Cohen's Kappa, and Bland–Altman plots. The results showed strong correlation and good agreement between the two indices at most stations. The correlation increased from short-term (1 and 3 months) to long-term (6, 9, and 12 months) scales. As a main finding, the gridded SPI in southeastern Iran has the capability to appropriately substitute station-based data for near-real-time drought monitoring.