Abstract: In order to achieve dynamic monitoring of drought conditions for summer maize across Huang-Huai-Hai Region, solar-induced chlorophyll fluorescence (SIF) data, the Self-calibrating Palmer Drought Severity Index (SCPDSI), and actual disaster records from 2001 to 2024 in this region are utilized to construct the drought dynamic monitoring thresholds for summer maize. To eliminate influences of solar radiation and canopy structure on SIF, the solar-induced chlorophyll fluorescence normalized by photosynthetically active radiation (SIFPAR) and yield of solar-induced chlorophyll fluorescence (Y-SIF) are calculated respectively. Response differences of SIF-derived indices to drought are compared using the method of correlation analysis. The indicator most sensitive to drought is then selected, and Gaussian fitting is utilized to construct fitting curves for different drought severity levels during the summer maize growth period. The average of fitting curves for adjacent drought levels is designated as the dynamic monitoring threshold curve for each drought level. The accuracy of the threshold curve is verified using typical drought records. These threshold curves are further applied in drought monitoring during the growth period of 2023 and 2024. Results indicate that SIF series indicators exhibit a single-peak trend throughout the summer maize growing season. Across all drought severity levels, the proportion of affected areas takes on a fluctuating yet decreasing trend in recent years. Both the spatial extent and occurrence frequency of drought events diminish as drought severity intensifies. SIF series indicators decrease as SCPDSI diminishes, exhibiting a positive correlation. During the summer maize growing period in Huang-Huai-Hai Region, the correlation coefficient between SIFPAR and SCPDSI is generally higher than that of SIF and Y-SIF. During the critical developmental stages in July and August, the spatial extent shows a positive correlation that exceeds 90%, showing that SIFPAR is particularly sensitive to drought conditions. Consequently, SIFPAR is selected as the indicator to monitor the functional changes of summer maize during drought periods. The dynamic drought monitoring threshold curve based on SIFPAR can accurately identify the occurrence and development of drought events, with the drought level identification accuracy rate of 87.93% compared to actual conditions. During 2023-2024 drought monitoring application, the dynamic monitoring thresholds consistently identified drought onset timing, severity, and spatial extent that closely aligned with actual disaster conditions. The dynamic monitoring thresholds can effectively capture the spatio-temporal evolution of drought across different developmental stages of summer maize. Results facilitate dynamic drought monitoring and provide data support for decision-making in Huang-Huai-Hai Region.