Winners of the 2025 International Cherry Blossom Prediction Competition!

We challenged contestants to predict the day that the cherry trees will reach peak bloom.

We asked contestants to submit their best predictions for select trees in Washington, D.C. and New York City (USA), Kyoto (Japan), Liestal-Weideli (Switzerland), and Vancouver, B.C. (Canada), along with a compelling narrative and reproducible analysis containing any data and code used. The competition is challenging because while it is known that cherry trees tend to bloom earlier each year as climates warm, complex weather patterns make annual predictions extremely difficult. 

For the fourth year in a row, students, researchers, and citizen scientists from around the world accepted our challenge. We will not be able to declare a winner until the cherry trees bloom later this year, but we are excited to share the predictions made by the contestants—as well as their AI competitor.

We are now thrilled to announce the results!

Humans win!

The average human entry was closer to the actual peak bloom date for every city except Kyoto. The humans were off by two days on average, while the AI was off by nearly four. Eight teams provided better predictions than the AI model and will receive commemorative memorabilia:

• Julijus Bogomolovas      
• Lukas Hans and Danielle Gagné 
• Michelle Harris 
• Frankie Hull 
• Garrett Martin 
• Grayson Saccomando 
• Matthias Spichiger 
• Manfred Tan 

Three teams also won awards in the categories: most accurate prediction, best model (biology), and best model (statistics). The team members will share more than $5,000 in total prize money.

Award for Most Accurate Prediction goes to Lukas Hans and Danielle Gagné

Lukas Hans and Danielle Gagné submitted the most accurate forecast. Their predictions deviated from the actual peak bloom dates by one day on average. To create their predictions, Lukas and Danielle combined a large number of meteorological variables—including daily maximum, mean, and minimum temperatures, daylight duration, precipitation sum, rain sum, and snowfall sum—from October to March each year. Predictions were then made using an ensemble method that combined support vector regression, Bayesian Ridge regression, XGBoost, and LightGBM. Congratulations Lukas and Danielle!

Award for Best Model (Biology) goes to Manfred Tan

Manfred Tan used Fast Dynamic Time Warping (FastDTW) to compare a given temperature pattern with historical patterns in order to determine the most probable position of a cherry tree in the bloom cycle. Temperature patterns were identified using cumulative degree days, rolling temperature sums, and the sum of squares of temperature values. The judges were impressed by the ingenuity of the approach, which has been used successfully during the COVID-19 pandemic to forecast cases and deaths. Congratulations Manfred!

Award for Best Model (Statistics) goes to Qinyu Xiong

Qinyu Xiong used a stacked Long Short-Term Memory (LSTM) neural network to capture the temporal dependencies exhibited in the data. The data included daily temperature data during the winter and spring as well as aggregated data and extremes. The neural network was trained using a categorical cross-entropy loss function. The judges praised the approach as powerful and dynamic. Congratulations Qinyu!

Honorable Mention goes to Jake Lane

Jake Lane used Google Trends, which measures what people search for, to predict the peak bloom date. Google Trends have been used to predict a wide range of phenomenon, from flu outbreaks to GDP. Judges considered the approach novel and creative. Congratulations Jake!

A big thanks to all competition participants

We know every contestant worked hard to produce their most accurate and interpretable predictions. All their work will help scientists better understand the impacts of climate change, and we hope their contribution does not end here. We encourage each contestant to continue to work on their models and narratives and reenter the Cherry Blossom Prediction Competition again next year.

We also keep a summary of all received entries for future reference.

A big thanks to our sponsors, partners, and judges. 

We thank the American Statistical Association, Washington Statistical Society, George Mason University’s Department of Statistics, and Columbia University’s Department of Statistics and Real World Data Science for their support, and partnerships with the International Society of Biometeorology, MeteoSwiss, USA National Phenology Network, the Vancouver Cherry Blossom Festival, Local Nature Lab, and WSP Eco Projects—as well as Mason’s Institute for Digital InnovAtion, Institute for a Sustainable Earth, and the Department of Modern and Classical Languages. We also thank our judges Lelys Bravo de Guenni, Mason Heberling, Nathan Lenssen, Will Pearse, Christine Rollinson, and Brittany Sutherland. Thank you! 

Organizers

Judges