CHIRPS v3.0 (Funk et al. 2026) replaced v2.0 for calibration and verification. Over Ethiopia, 1993–2025, v3.0 is about 15% wetter (consistent with its gauge-undercatch correction) while each season's share of annual rainfall and the ranking of years agree closely, so tercile limits move up. The same method was refitted on v3.0: the decision gates give the same outcome, and skill is similar overall. Each version is scored against its own observations.

1. 1

   Download

   ECMWF SEAS5 (system 51) daily accumulated precipitation from the Copernicus CDS for the initialization month, 1993 to the forecast year.

   download_seasonal_forecasts_daily_c3s.py
2. 2

   Inspect

   Inventory every year: initialization, lead times, member counts and accumulation increments (GRIB packing tolerance 0.2 mm).

   inspect_inputs.py
3. 3

   Prepare

   De-accumulate to daily totals, sum the season and each month; CHIRPS on the same dates.

   prepare_seasonal.py · run_monthly.py
4. 4

   Regrid

   Each 1° model cell is copied into its 4×4 block of 0.25° CHIRPS cells (no interpolation).

   regrid_seasonal.py
5. 5

   Calibrate

   Per-cell amount correction, smoothed tercile counts and one climatology-blend weight per target.

   final_shared_blend.py
6. 6

   Evaluate

   Nested cross-validation 1993–2016; exploratory fixed-fit scores for later years; whole-year significance tests.

   local_blend.py · decision_gates.py
7. 7

   Deliver

   Maps, summaries and an offline gallery; forecasts frozen with SHA-256 hashes before observations are used.

   run_operational.py · build_season_products.py
8. 8

   Verify

   Official CHIRPS v3.0 monthly files, checked against the archive where they overlap, scored per month and season.

   prepare_verification_2026.py · verify_frozen_2026.py

Every stage is a script in scripts/; documents in docs/ record each step, issue and decision (runbook: docs/38; per-season guides: docs/40).

1. Amount correction per cell, equal weight per year: x̂ = max(0, μobs + r·(x − μmodel)), r = clip(σobs/σmodel, 0.5, 2)
2. Tercile probabilities from the corrected members against CHIRPS tercile limits, smoothed: (n + 0.5)/(M + 1.5)
3. Climatology blend: pfinal = (1 − λ)·pmodel + λ·pclimatology, with one λ per target fitted on leave-one-year-out RPS.
   pclimatology is, for each grid cell, the observed frequency of each tercile in the training reference years (about one-third each; it differs slightly because of sample size, category limits and ties).
4. Skill score: RPSS = 1 − RPSforecast / RPSclimatology; RPSS +0.05 means a 5% lower ranked probability score than climatology. CRPSS is the same for rainfall amounts.

Rainfall amounts are corrected cell by cell with mean–variance scaling (also called simple bias adjustment or variance scaling; Teutschbein & Seibert 2012; Torralba et al. 2017; Manzanas et al. 2019): each member is shifted to the CHIRPS mean and its anomaly rescaled by the ratio of CHIRPS to model standard deviation (equations above). It corrects the mean and spread, not the shape of the distribution; it is not quantile mapping. The model mean and spread are pooled over all members and years, with equal weight per year (25 members to 2016, 51 from 2017); training years 1993–2025.

Probabilities are the share of corrected members in each CHIRPS tercile (ensemble relative frequency; Wilks 2019), smoothed so no category is 0 or 1, then blended with climatology. Weighting a model against climatology follows Rajagopalan et al. (2002) and Robertson et al. (2004); here one weight per target is fitted by minimising the ranked probability score of leave-one-year-out probabilities.

Scores: ranked probability score (Epstein 1969) and continuous ranked probability score (Hersbach 2000), as skill scores against climatology. Forecasts: ECMWF SEAS5 (Johnson et al. 2019); observations: CHIRPS v3.0 (Funk et al. 2026), v2.0 before 11 October 2026 (Funk et al. 2015).

Choices of this project, not taken from a single paper: the 0.5–2 limits on the spread ratio, mean-only correction where the model spread is below 1 mm, clipping at 0 mm (its weakness in dry months, issue I-08), equal weighting of years, the count smoothing and one blend weight shared by all cells. A square-root-space amount correction passed the gate for amounts and is recommended for the next cycle (docs/36). Details: docs/46. DOIs checked on Crossref, 11 October 2026.

1. Epstein, E. S. (1969). A scoring system for probability forecasts of ranked categories. Journal of Applied Meteorology, 8(6), 985–987. doi:10.1175/1520-0450(1969)008<0985:ASSFPF>2.0.CO;2
2. Funk, C., et al. (2015). The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes. Scientific Data, 2, 150066. doi:10.1038/sdata.2015.66
3. Funk, C., et al. (2026). The Climate Hazards Center Infrared Precipitation with Stations, Version 3. Scientific Data, 13, 718. doi:10.1038/s41597-026-07096-4
4. Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15(5), 559–570. doi:10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2
5. Johnson, S. J., et al. (2019). SEAS5: the new ECMWF seasonal forecast system. Geoscientific Model Development, 12, 1087–1117. doi:10.5194/gmd-12-1087-2019
6. Manzanas, R., et al. (2019). Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset. Climate Dynamics, 53, 1287–1305. doi:10.1007/s00382-019-04640-4
7. Rajagopalan, B., Lall, U., & Zebiak, S. E. (2002). Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Monthly Weather Review, 130(7), 1792–1811. doi:10.1175/1520-0493(2002)130<1792:CCFTRA>2.0.CO;2
8. Robertson, A. W., Lall, U., Zebiak, S. E., & Goddard, L. (2004). Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Monthly Weather Review, 132(12), 2732–2744. doi:10.1175/MWR2818.1
9. Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. Journal of Hydrology, 456–457, 12–29. doi:10.1016/j.jhydrol.2012.05.052
10. Torralba, V., et al. (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology, 56(5), 1231–1247. doi:10.1175/JAMC-D-16-0204.1
11. Wilks, D. S. (2019). Statistical Methods in the Atmospheric Sciences (4th ed.). Elsevier.

A change is adopted only if it improves the cross-validated 1993–2016 scores (Holm-adjusted p < 0.05 over the five JJAS targets) and does not worsen 2017–2025. Also evaluated and not adopted: regularized Dirichlet recalibration and per-cell or regime-specific blend weights.

Calibration parameters are fitted mostly on 25-member hindcasts and applied to 51-member forecasts. In 2017–2025 the 51-member forecasts scored about the same as, or slightly better than, 25-member subsets, so the transfer did not degrade scores in the years tested; this does not show it is neutral in every year or region. The corrected ensemble is under-dispersive (spread smaller than error), which the climatology blend partly offsets. Monthly and seasonal outlooks are calibrated separately: for JJAS 2026 over all Ethiopia the corrected monthly means sum to 432 mm against 430 mm for the season (+1.7 mm).

• Every forecast file is inventoried; re-downloaded years were bit-identical, confirming SEAS5 system 51.
• Small negative daily increments (≤ 0.15 mm) are GRIB packing rounding; tolerated up to 0.2 mm and clipped, and the seasonal sum is checked.
• CHIRPS sea cells (223) are always missing; no partial season enters any fit.
• Verification observations are compared with the archive in the overlap year (tolerance 0.0001 mm/day) before use.

• Few evaluation years and single verification seasons give wide uncertainty; regional and monthly results are noisy.
• The later evaluation years were inspected during method development; the cleanest evidence is the cross-validated 1993–2016 score.
• The 0.25° maps repeat 1° model information; they add no dynamical detail.
• Cycles before 2027 are research reconstructions produced after their initialization dates; from 2027 each forecast is issued and frozen before its season (docs/52). None is an official EMI or ICPAC forecast.

git clone https://github.com/YonSci/calibrated_seasonal_rainfall.git
python -m venv .venv && .venv\Scripts\activate && pip install -r requirements.txt
set CALIBRATION_CYCLE=config\cycles\sep_2026_ondj.json
python scripts\run_operational.py --config config\cycles\sep_2026_ondj.json --workflow all
python scripts\build_site.py

Raw ECMWF and CHIRPS data are not stored in the repository. Step-by-step guides: docs/40; project status: docs/36.