<p>Drought poses significant challenges to agriculture, water resources, and ecosystems in Morocco, where monitoring and managing drought is important for economic and social stability. This study develops a new Combined Drought Anomaly Index (CDAI) for monitoring agricultural drought in Morocco, integrating multiple remote sensing-based indices using Principal Component Analysis. The CDAI incorporates anomaly indices derived from Land Surface Temperature, Normalized Difference Vegetation Index, Evapotranspiration, and Precipitation data to assess spatiotemporal drought patterns from 2000 to 2022. The study focused on rainfed cereal-producing areas across 33 Moroccan provinces. The CDAI was validated against two independent drought indices: Detrended Standardized Cereal Yield (DSCY) and in situ Standardized Precipitation Index (SPI). A comparison of the performance of early agricultural drought detection between the proposed index and commonly used indices, such as the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), was assessed as part of the CDAI validation process. Validation results demonstrated strong correlations between the CDAI and DSCY, with approximately 76% of provinces exhibiting correlation coefficients ranging from 0.60 to 0.89. Moreover, 33% of the provinces representing the most productive cereal-growing regions nationally showed correlations exceeding 0.8 (p-value &lt; 0.01) on a seasonal scale. The overall correlation reached 0.87. Similarly, CDAI exhibited strong agreement with SPI-3 and SPI-6, with correlation values reaching 0.90 in both December and January, and 0.89 in February. Furthermore, CDAI outperformed the VCI, TCI, and VHI in both accuracy and stability during the early agricultural drought detection period (January to March). These findings confirm that the CDAI effectively captured both temporal and spatial variations in drought conditions. After robust validation, the application of the CDAI indicated that Morocco experienced drought conditions during 32% of the study period (2000–2022), with 5.25% of the time categorized as exceptional drought. The 2015–2016 agricultural season, particularly December 2015, was identified as the driest period, while the 2008–2009 season was the wettest. Overall, the CDAI provided a comprehensive and timely assessment of drought conditions across Morocco’s rainfed cereal-producing areas, contributing valuable insights for early warning systems and agricultural risk management.</p> Graphical Abstract <p>Based on the graphical abstract, this study aimed to develop a new index for monitoring agricultural drought over Morocco. The objective was to create a robust and reliable index capable of detecting drought impacts several months in advance and across different agro-climatic zones, which helps improve agricultural drought management strategies. As illustrated in the graphical abstract, the study integrates multiple relevant and publicly available remote sensing datasets that are key indicators of agricultural drought: water availability (precipitation from CHIRPS), heat stress (evapotranspiration and land surface temperature from MODIS), and vegetation health (NDVI from MODIS). To synthesize the information and reduce dimensionality, Principal Component Analysis (PCA) was applied, resulting in the development of a new anomaly-based composite index called the Combined Drought Anomaly Index (CDAI). The effectiveness of CDAI was evaluated using several validation approaches, including comparisons with independent agricultural drought indicators such as cereal-yield-based indices for 33 provinces, the Standardized Precipitation Index (SPI) at multiple time scales, and widely used remote sensing indices like VHI, TCI, and VCI. Historical drought events were also used for qualitative validation. The validation results confirmed the strong performance of CDAI in detecting and monitoring agricultural drought. Statistically, the CDAI showed high correlations, reaching up to 0.89 with the cereal yield-based index and 0.90 with SPI-3, both at a 99% confidence interval. Moreover, CDAI demonstrated superior capacity in detecting agricultural drought up to four months in advance compared to existing indices. Following validation, a spatio-temporal analysis was conducted using CDAI over the period 2000–2022. The results revealed that 32% of the monthly time series was affected by drought, with 5.25% of those months classified as experiencing exceptional drought. December 2015 emerged as the most severely affected month. Overall, the CDAI demonstrates strong potential for operational use in agricultural drought monitoring and early warning systems.</p> <p></p>