Enhanced seasonal soil moisture forecasts by integrating APCC multi-model ensemble predictions into a land surface model framework
Soil moisture is a key driver of climate variability and extremes such as heatwaves and wildfires, and accurate prediction at seasonal timescales is therefore essential. Skillful forecasts at these scales, however, remain a significant challenge. While multi-model ensemble (MME) climate forecasts consistently outperform individual models for atmospheric variables, their soil moisture outputs require careful interpretation when combined across models because of differences in land surface model (LSM) structures and soil configurations. To bridge this gap, we developed a seasonal soil moisture prediction system using the Joint UK Land Environment Simulator driven by NCEP CFSv2 meteorological forecasts, and integrated monthly temperature and precipitation forecasts from the APCC MME—which exhibits superior seasonal prediction skill to single dynamical models—into the system’s meteorological forcing. Despite correcting only temperature and precipitation—the two variables consistently available across all participating MME models—the approach yielded substantial improvements in soil moisture forecast skill. Retrospective hindcast experiments for 1991–2016, initialized in February, show that the MME-corrected forecast (J-MME) substantially outperforms the CFSv2-driven baseline (J-CFSv2) in predicting boreal spring–summer soil moisture. The global average coefficient of determination (R2) between forecast and reanalysis increased by 0.10–0.12 at three- to four-month lead times. The improvements were driven by precipitation correction in water-limited regions and temperature correction in energy-limited high-latitude regions. This approach also extended the effective lead time for statistically significant predictions and improved the detection of historical drought events, demonstrating that even limited integration of MME information into an LSM framework can yield meaningful gains in seasonal soil moisture and drought forecasting.