###### Abstract
However, most previous drought monitoring studies have focused on monthly scales, with fewer addressing daily scales. [PERSON] et al. [20] developed a daily SPI to address the limitations of temporal resolution in existing monthly drought assessments. The daily drought index overcomes the limitations of the monthly index, providing more detailed information on drought conditions and enabling the monitoring of drought characteristics across multiple time scales, including 30-day, 60-day, 90-day, and longer periods. Therefore, enhancing the temporal resolution of drought indices is essential in accurately analyzing drought characteristics. Drought assessment must be data-driven, and traditional methods of drought monitoring primarily rely on field observations and meteorological assessments. However, obtaining sufficient drought monitoring sampling sites on a regional scale is labor-intensive, and field sampling faces methodological challenges [21]. Additionally, the use of other variables, such as temperature and precipitation, in meteorological assessments is often limited by the lack of sites data [22; 23]. With the continuous advancement of science and technology, the emergence of exogenous data has provided new technical means of drought monitoring. Satellite remote sensing data have emerged as an effective and reliable tool in this context [24; 25; 26]. Over the last several decades, scholars have developed precipitation products with varying spatial and temporal resolutions by integrating international meteorological satellite data, machine learning algorithms, and data assimilation techniques [27]. Despite the availability of numerous precipitation datasets, their accuracy often falls short of the requirements for current drought event assessments. For example, ERA5, one of the most widely used precipitation datasets, has a spatial resolution of only 0.1\({}^{\circ}\), which may not be sufficient for detailed drought analysis [28]. There have been many studies using precipitation data developed from pavement models with high spatial resolution. However, the land surface model requires multiple models to drive the generation of precipitation data, and this data generation has been taking place since 2008 [29]. As a pivotal agricultural production base in China, the Northeast region is crucial in national food security [30]. However, the Northeast region experiences significant spatial and temporal fluctuations in precipitation and is vulnerable to drought. Over the past 20 years, 25% of the total cultivated area has been affected by drought, posing a serious threat to the economic development of the region [31,32]. Studies have indicated that the Northeast region has experienced a significant aridification trend since 1961 [33]. The frequency and intensity of drought events in the Northeast have increased notably after 2000 compared to the pre-2000 period [34]. Climate model projections under the RCP8.5 scenario indicate that the summer PDSI in the region is expected to decline by 2.5-3.0 units by the end of the 21 st century, suggesting a continued escalation in drought risk [35,36]. Additionally, numerous global-scale studies have confirmed that drought is a dominant factor in the decline of vegetation productivity [37]. With the increasing frequency of drought events in Northeast China, their impact on regional vegetation growth and ecosystem stability has become increasingly pronounced [38,39]. The effects of drought on vegetation growth in Northeast China exhibit significant regional differences [39]. As the primary commercial grain base in China, the Northeast region accounts for approximately 25% of the country's grain production, and drought poses a significant threat to national food security [40]. Research on drought in this region not only provides a scientific basis for enhancing China's food security and water resource management but also serves as a reference for the sustainable development of other regions in similar climatic zones.