Generation of 1 km high resolution Standardized precipitation evapotranspiration Index for drought monitoring over China using Google Earth Engine

• A novel random forest approach is proposed to enhances SPEIbase resolution from 0.5° to 1 km. • Data gridding and sample balancing in preprocessing step enhance the accuracy. • The proposed method yields stable, high-precision 1 km SPEI with strong spatiotemporal alignment to SPEIbase. • Utilizing GEE's public data significantly lowers demands for meteorological data quantity and quality. Under the background of climate change and global warming, extreme drought events in China are becoming increasingly frequent. Drought is one of the primary natural causes of damage to China’s agriculture, economy, and environment, making timely, accurate, and high-resolution drought monitoring particularly crucial. The global standardized precipitation − evapotranspiration index database (SPEIbase) is a widely accepted and used global-scale drought monitoring product. However, limited by its spatial resolution of 0.5 degrees, it is difficult to describe the local spatio-temporal structure of drought. How to improve its spatial resolution while maintaining spatio-temporal consistency is one of the current research hotspots. Based on the response of vegetation growth status to drought, this paper proposes a simple and feasible SPEI prediction method, which improves the resolution of SPEIbase from 0.5 degrees to 1 km. Sixteen remote sensing inversion indices, reflectance and elevation data related to drought were selected from Google Earth Engine (GEE) as features. After preprocessing such as gridding and sample balancing, a random forest regression model was constructed to achieve high spatial resolution prediction of SPEI. SPEI with time scales of 1, 3, 6, 9, 12 and 24 months in July 2020, August 2019 and August 2018 in China was selected for experiments. The accuracy of 1 km resolution SPEI was evaluated through metrics such as root mean square error (RMSE), Pearson correlation coefficient (PCC) and determination coefficient (R 2 ). At the same time, it was compared with the existing 1 km resolution SPEI dataset and the site-scale SPEI values. The results show that the method in this paper can obtain accurate prediction results more stably. The PCC and R 2 of different months and multiple time scales are all higher than 0.9 and 0.8, and the RMSE is lower than 0.4, showing a good application prospect. Despite the good consistency between the Proposed SPEI and SPEIbase with the site-scale SPEI values, there is still significant room for improvement.