Abstract
Reliable prediction of the solar cycle is a formidable challenge, yet it is increasingly vital in our technology-dependent society as solar activity drives space weather. Various methods, including precursors, nonlinear curve fitting and extrapolation, statistical and Machine Learning (ML) models, and dynamo and surface flux transport (SFT) models, were implemented to predict past cycles. Analysing about 100 predictions for Solar Cycle 24 and over 130 for Solar Cycle 25, we find that most methods largely failed to predict the peak correctly: Cycle 24 was statistically predicted to be a strong cycle, whereas Cycle 25 was predicted to be a weak cycle. By and large, predictions made only after the cycle began became closer to reality. ML-based models also produced discouraging results. The polar field and its proxy-based predictions are the most physically supported approach to prediction; however, applying them much earlier, before the solar minimum, may yield inaccurate results. Dynamo models are progressively improving both in understanding and in forecasting; however, they need to improve by accurately assimilating the observed polar field data and additional physics, such as meridional flow variations. Solar dynamo theory, complemented by the SFT model and observations, demonstrates that the prediction of a cycle before the time of its previous cycle’s maximum is meaningless. The current solar cycle is declining, and the community is now preparing for the prediction of the next cycle. Thus, this review will guide future studies.
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Data Availability
The data used/produced in the present study are available from the author upon reasonable request.
Notes
Several cycles, including Cycle 24, display multiple peaks (Karak et al. 2018a). Therefore, assigning a number to the peak of a cycle is subjective, but we take the highest peak value as obtained from the 13-month smoothed ISSN V2.0 data, which is also commonly used in the literature to measure the strength of the cycle.
The polar field is the radial component of the poloidal field measured near the polar regions.
In contrast to the local or small-scale dynamo, the length- and time-scales of the generated magnetic field in the large-scale dynamo is much larger than that of the driver—the convective flow.
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Acknowledgements
The author is indebted to Rambahadur Gupta, Anu Sreedevi, and Bidisha Dey for their assistance in preparing several figures and collecting/cross-checking the predicted values of solar cycles 24 and 25. Ram and Anu helped me considerably in extracting several references for solar cycle predictions. I am also grateful to Arnab Rai Choudhuri (my PhD supervisor), Jie Jiang, Piyali Chatterjee, and Dibyendu Nandi, from whom I have learned most of the fundamentals of the solar dynamo during my PhD, which enriched my understanding of the solar cycle and its prediction, as reflected in this review. Now I acknowledge financial support provided the Indian Space Research Organisation (project no. ISRO/RES/RAC-S/IITBHU/2024-25) and the Anusandhan National Research Foundation (ANRF) through the MATRIC program (file no. MTR/2023/000670) and the computational resources of the PARAM Shivay Facility under the National Supercomputing Mission, the Government of India, at the Indian Institute of Technology Varanasi. SOHO is a project of international cooperation between ESA and NASA. Courtesy of NASA/SDO and the HMI science teams. The group sunspot number data are obtained from SIDC/SILSO (https://www.sidc.be/SILSO/DATA/GroupNumber/) and the sunspot number data are from World Data Center (WDC)-SILSO, Royal Observatory of Belgium, Brussels, International Sunspot Number V2.0 (Clette and Lefèvre 2015). The “Flare Index" dataset was prepared by the Kandilli Observatory and Earthquake Research Institute at the Bogazici University and made available through the NOAA National Geophysical Data Center (NGDC; https://www.ngdc.noaa.gov/stp/space-weather/solar-data/solar-features/solar-flares/index/).
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Karak, B.B. Solar cycle prediction: challenges, progress, and future perspectives. Rev. Mod. Plasma Phys. 10, 11 (2026). https://doi.org/10.1007/s41614-026-00220-2
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DOI: https://doi.org/10.1007/s41614-026-00220-2