SMART-AI: A High-Performance Prediction Method for Seasonal Rainfall in China Based on the Impacts of Climate Modes and the Artificial Intelligence

Seasonal droughts and floods are among the world’s most severe natural disasters, threatening socioeconomic development and human life and property, making accurate seasonal prediction a key need for disaster risk reduction. Yet, the high complexity of precipitation anomalies driven by multi-scale variations, multiple factors, and atmospheric nonlinear chaos, which makes such forecasts a global challenge. Climate modes (e.g., ENSO), as major drivers of seasonal precipitation anomalies, are essential predictors. Systematically assessing their contributions to predictability and developing prediction methods based on their influence are therefore of great scientific and practical value for understanding and forecasting seasonal droughts and floods.

Addressing seasonal rainfall prediction challenges, the SMART, the acronym for Singular predictable climate Modes (SM) and Anomalous Relative Tendency (ART), climate prediction principle is proposed by Prof. Xiu-Qun Yang from Nanjing University. It contains 4 major steps: 1. Online temporal-scale separation (ART); 2. Extracting optimal singular climate modes (SM); 3. Constructing SMART model based on SM and ART; 4. Predicting with ART and Recent Background Anomalous (RBA). This study develops two ensemble prediction methods based on SMART principle, which combine the impacts of climate modes on China’s flood-season precipitation anomalous relative tendencies (ART) with multiple artificial intelligence (AI) models and multi-parameter perturbation scheme, including the SMART Optimal combined Multiple AI Method (OMAI) and SMART Ensemble AI Method (EAI). These two methods demonstrate significant predictive skill improvements over MME direct predictions. For example, using 160 stations historical precipitation data in China and historical circulation datasets, multiple key tropical and extratropical climate modes affecting to the ART of China’s flood-season (JJA) precipitation are extracted by SVD method. The SMART-OMAI method integrates these modes with multiple AI models, while SMART-EAI incorporates multi-parameter perturbations with LSTM model. Independent validation for flood-season precipitation anomalies in China during 1994-2016 via these two methods, yields PS scores of 76.5 and 76.4, respectively over 5% higher than dynamical-statistical models (73.2) and over 20% better than direct MME direct predictions (63.5). Anomaly correlation coefficients reach 0.16 and 0.14, marking qualitative improvements over MME direct predictions (0.01), with notable temporal correlation enhancements in North China and Northeast China. By merging the physical basis of the climate modes with non-linear predictive strengths of AI models, these two AI-based prediction methods offer a scientifically robust and practical solution, that is, a SMART solution for seasonal rainfall prediction in China.