Seamless seasonal to multi-annual predictions of temperature and Standardized Precipitation Index by constraining transient climate model simulations

Abstract. Seamless climate predictions integrate forecasts across various timescales to provide actionable information in sectors such as agriculture, energy, and public health. While significant progress has been made, there is still a gap in the continuous provision of operational forecasts, particularly from seasonal to multi-annual timescales. We demonstrate that filling this gap is possible using an established climate model analog method to constrain variability in CMIP6 climate simulations. The analog method yields predictive skill for surface air temperature forecasts across timescales, ranging from seasons to several years. On average, the analog-based surface air temperature predictions provide added value over the unconstrained CMIP6 ensemble, especially on seasonal to annual timescales. Similar to operational climate prediction systems, Standardized Precipitation Index forecasts are less skillful than surface air temperature forecasts but still better than the CMIP6 unconstrained simulations. The analog-based seamless prediction system shows very similar patterns of skill compared to state-of-the art initialized climate prediction systems and has competitive skill on annual and biennial forecast ranges. While the current prediction systems provide only 1–2 initializations per year, the analog-based system can easily provide predictions with monthly initializations, delivering seamless climate information throughout the year currently not available from traditional seasonal or decadal prediction systems. Furthermore, due to analog-based predictions being computationally inexpensive, we argue that these methods are a valuable and viable complement to existing operational prediction systems.