What is Super Typhoon Champi and when will it reach the Philippines?

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1. 1

   Philippine archipelago and South China Sea monsoon plus ocean cooling buffer Northwestern Pacific super typhoons.

   Nature communications (Ma et al.)Published Aug 11, 2025Checked Oct 18, 2026

   “In dramatic contrast to the continuous intensification of strong Atlantic hurricanes moving into the Gulf of Mexico, super typhoons (SuperTYs) originating from the Northwest Pacific significantly weaken after entering the South China Sea (SCS). Using an idealized cyclone intensity model, we show that this striking discrepancy results from the SCS's buffering effects, including persistent topographic blocking by the Philippine archipelago and seasonally shifting atmospheric-oceanic constraints. The archipelago accounts for ~54% of typhoon weakening throughout the season, while environmental factors explain the remaining ~46% with the significant seasonal dependence on strong SCS monsoon-induced vertical wind shear in summer (~30%) and ocean cooling in autumn (~37%). The Coupled Model Intercomparison Project Phase 6 projects that these buffering effects may likely persist through the 21st century. These findings enhance our understanding of the SCS coastal vulnerability to typhoon impacts, with broader implications for climate risk management.”

2. 2

   Development and Evaluation of Multi-week Tropical Cyclone Strike Probability Forecasts in the Philippines

   The Philippine journal of science (Adelino et al.)Published Jul 18, 2023Checked Oct 18, 2026

   “To assess its performance, real-time-derived TC strike probability forecasts over the Tropical Cyclone Information Domain (TCID) of the Philippines (bounded by 0–27 °N and 110–155 °E) covering the period from 06 Oct 2020–31 Oct 2022 were evaluated. Verification metrics revealed that the skill and reliability of the TC forecasts vary with lead time and the TC being forecasted. Week 1 forecasts are reliable and can be helpful for decision-making, whereas Week 2 forecasts are considered most reliable only up to the 51–60% probability interval. On the other hand, forecasts with 3–4-wk lead times are reliable for probabilities less than 20%. The case study performed using five TCs with different intensity classifications has shown that generally, forecasts for TCs with stronger intensity have higher skill than forecasts for relatively weaker ones. It was also observed that the magnitude of the probability values varies with the intensity changes within the validity period. These findings suggest that multi-model ensemble forecasts can be utilized for the improvement and eventual operationalization of multi-week TC strike probability forecasts over the Philippines.”

3. 3

   Evaluation of Multiweek Tropical Cyclone Forecasts in the Philippines

   Weather and Forecasting (Tierra et al.)Published Aug 30, 2023Checked Oct 18, 2026

   “Results revealed that the ECMWF model is consistently the most skillful in multiweek TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p < 0.05) positional bias toward the right of observed tracks beyond week 1, and that they tend to propagate slower than observations especially in week 1 and week 2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multiweek TC forecasts in the Philippines.”

4. 4

   Challenges in forecasting super typhoon Yagi (2024)

   Tropical Cyclone Research and Review (Li et al.)Published Aug 23, 2025Checked Oct 18, 2026

   “It maintained supertyphoon intensity for 67 hours, with hurricane-force winds (> 32.7 m·s -1 ) persistently affecting Hainan's land area for approximately 10 hours. These characteristics exceeded those of Supertyphoon Rammasun and pose great challenges in operational forecasting. Operational numerical weather prediction (NWP) models show marked disagreements in predicting the track of Yagi in the early stage. In the later period, while the ECMWF and Pangu model predictions suggest landfall over northeastern Hainan, those of the NCEP-GFS and three CMA models maintain a landfall over the Leizhou Peninsula. Given the fact that historically, the landfall probability in Guangdong is much higher than that in Hainan, such discrepancies considerably increased the difficulty in determining the landfall location. The forecasting of Yagi’s intensity also poses a substantial challenge because of the rare occurrence of supertyphoon landfall cases in Hainan and the underpredicted intensities from the NWP models.”

5. 5

   Analog forecasting of tropical cyclone rainfall in the Philippines

   Weather and Climate Extremes (Bagtasa)Published Apr 26, 2021Checked Oct 18, 2026

   “To predict TC rainfall, the mean rainfall of all past TCs with similar tracks included in the database, referred to as analog TCs, is calculated. Landfalling TCs from 2016 to 2018 are used to optimize the selection of past analog TCs. Each past TC member was also adjusted according to a target TC's intensity and movement speed. The optimized analog method is then applied to landfalling TCs from 2019 to November 2020. Results show that the composite rainfall from past TCs within 1.8∘ of the forecast TC yields the best hit rate of intense rainfall. The analog TC rainfall forecast generally has a similar spatial distribution as the observed TC rain. However, this method tends to miss extreme rainfall values due to a “smoothing” effect caused by the variability of extreme rain locations of each TC member and constraints in the rainfall data used in the database. Nevertheless, forecast assessment results show that analog TC rainfall forecasting performed better than the WRF model in predicting intense and inland rainfall. In addition to it being computationally inexpensive, it can complement the inherent biases of dynamical models.”