###### Abstract  
Drought significantly reduces global agricultural productivity and destabilizes ecosystems. As the primary grain-producing region and a key ecological buffer zone in China, Northeast China is experiencing intensifying drought stress. However, the regional-scale characteristics of refined drought and the impact mechanisms on different types of vegetation in the Northeast are rarely investigated. In this study, we analyzed the spatial and temporal characteristics of drought over 30-, 60-, 90-, 180-, 270-, and 360-day time scales in Northeast China using the Standardized Precipitation Index (SPI) based on high-precision daily precipitation data simulated by CLM3.5 from 2008 to 2023. Additionally, we used the MODIS Normalized Difference Vegetation Index (NDVI) to elucidate the response of vegetation to drought across different land use types. The results showed that SPI-30 was the most sensitive for drought detection, and there was a clear trend of drought aggravation in the northern part of the Northeast region. The strongest correlation between vegetation and drought was found in September. A significant lag in the response of vegetation to drought was observed in May, June, July, and August, with the best correlation observed at a one-month lag. In addition, the degree of response to drought varies among different types of vegetation. Grasslands are the most sensitive to drought, while woodlands and wetlands have a weaker response. This study provides a reference for assessing the dynamics of refined climates at different spatial and temporal scales and offers actionable insights for ecosystem management in climate-sensitive agricultural regions. d +
Footnote †: journal: _Nustainability_  
0000-0002-4307-2270]Y.X.Zhang 0000-0002-0002-3330]Y.X.Zhang 0000-0002-0002-3330]Y.X.Zhang 0000-0002-3330-3300]Y.X.Zhang 0000-0002-3330-3300]Y.X.Zhang 0000-0002-33300-3300-33000]Y.X.Zhang 0000-0002-3330-33000]Y.X. 200,000 km\({}^{2}\) of cropland was affected by drought annually from 1984 to 2018 [7]. In the context of climate change, the unpredictability of the duration and intensity of global extreme events is increasing [8]. The Sixth Assessment Report of the IPCC projects that future drought events will have broader and more intense impacts [9]. Additionally, the intervals between droughts and other extreme weather events, such as heat waves, are expected to shorten, posing greater challenges to regional resilience [10]. Therefore, identifying drought events and investigating changes in their characteristics is crucial in mitigating drought losses and providing a scientific basis for drought management and decision-making. Droughts are typically classified into four primary types: meteorological, agricultural, hydrological, and socio-economic [11]. Meteorological drought, the most common type, serves as the driving force for other drought types [12]. Identifying and characterizing meteorological drought is a crucial component of drought research. Several widely used drought indices include the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Palmer Drought Severity Index (PDSI) [13]. Despite their widespread use, these indices have limitations. The PDSI, which integrates temperature, precipitation, and soil factors, is widely used for agricultural drought assessment but is highly sensitive to soil information and has fixed time scales [14; 15; 16]. The SPEI, which combines precipitation and potential evapotranspiration, effectively accounts for moisture deficits but requires complete data, lacks sufficient temporal continuity for drought monitoring, and is calculated on a monthly scale, limiting its ability to accurately identify drought onset and termination [17]. In contrast, the SPI, which requires only precipitation data, quantifies precipitation deficits or surpluses on any time scale, providing a more accurate assessment of drought conditions [18; 19].