{"id":479395,"date":"2026-10-16T03:49:18","date_gmt":"2026-10-16T10:49:18","guid":{"rendered":"https:\/\/climatescience.press\/?p=479395"},"modified":"2026-10-16T03:49:20","modified_gmt":"2026-10-16T10:49:20","slug":"nearly-600-california-quakes-prove-next-day-earthquake-forecasts-actually-work","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=479395","title":{"rendered":"Nearly 600 California Quakes Prove Next-Day Earthquake Forecasts Actually Work"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"485\" data-attachment-id=\"479396\" data-permalink=\"https:\/\/climatescience.press\/?attachment_id=479396\" data-orig-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?fit=1168%2C784&amp;ssl=1\" data-orig-size=\"1168,784\" data-comments-opened=\"1\" data-image-meta=\"{&quot;credit&quot;:&quot;0149c8f6-45d5-4251-be08-c9883c98e464&quot;,&quot;caption&quot;:&quot;Signature: 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&quot;}\" data-image-title=\"0 Nearly 600 California Quakes Prove Next-Day Earthquake Forecasts Actually Work\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Signature: 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&lt;\/p&gt;\n\" data-large-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?fit=723%2C485&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?resize=723%2C485&#038;ssl=1\" alt=\"Infographic detailing earthquake data analysis in California, emphasizing nearly 600 earthquakes studied to validate next-day earthquake forecasts, with visual elements showing fault lines, statistical models, and performance summaries.\" class=\"wp-image-479396\" srcset=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?resize=1024%2C687&amp;ssl=1 1024w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?resize=300%2C201&amp;ssl=1 300w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?resize=768%2C516&amp;ssl=1 768w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?resize=640%2C430&amp;ssl=1 640w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-Nearly-600-California-Quakes-Prove-Next-Day-Earthquake-Forecasts-Actually-Work.jpg?w=1168&amp;ssl=1 1168w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><figcaption class=\"wp-element-caption\">AI generated by Grok<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>A decade- long prospective evaluation of earthquake forecasting models in California, tested against nearly 600 quakes (exactly 597 of magnitude Mw \u2265 3.95), shows that select models, especially certain versions of well- established operational ones, can reliably describe next- day patterns of earthquake numbers, locations, and magnitudes.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This comes from a University of Bristol-led study published in <em>Nature Communications<\/em> (August 2026; press coverage around October 14- 15, 2026). Researchers assessed 27 time- varying seismicity models (focusing on 21 for Mw \u2265 3.95) operated fully prospectively by the <strong>Collaboratory for the Study of Earthquake Predictability (CSEP)<\/strong> from August 2007 to August 2018. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These generated 51,625 automated next- day forecasts for California, using only data available before each forecast day. The forecasts were compared to the 597 observed earthquakes (depths \u2264 30 km in the CSEP- California test region), which included major sequences such as the 2010 Mw 7.2 El Mayor- Cucapah, the 2012 Brawley swarm, and the 2014 Mw 6.0 South Napa event.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Key Findings<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Roughly <strong>95% <\/strong>of the models produced forecasts statistically consistent with the <strong>number<\/strong> of observed earthquakes (using a Negative Binomial Distribution test that better handles clustering variability; a stricter Poisson test was less favorable overall).<\/li>\n\n\n\n<li>About <strong>two- thirds<\/strong> were consistent with the <strong>spatial locations<\/strong> (epicenters) of the quakes.<\/li>\n\n\n\n<li>About <strong>95%<\/strong> matched the <strong>magnitude distributions<\/strong>.<a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7\" target=\"_blank\" rel=\"noopener\">\u2060<\/a><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Individually, select versions of the <strong>Epidemic- Type Aftershock Sequence (ETAS)<\/strong> and <strong>Short- Term Earthquake Probability (STEP)<\/strong> <strong>models<\/strong>, widely used types for <strong>operational earthquake forecasting (OEF) <\/strong>by agencies in places like Italy, New Zealand, and the US, showed sustained consistency with the prospective data. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Some newer models and those incorporating smaller earthquakes as inputs performed relatively better, indicating progress in forecasting skill over time. Many models slightly overestimated rates but stayed within statistical bounds cumulatively; daily performance was stronger in quieter periods or certain aftershock sequences and weaker right after the largest mainshocks (as expected, since these models struggle to anticipate the exact timing of large events themselves).<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Context and Implications<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">These are probabilistic forecasts of rates and likelihoods (where, when, how large activity is more likely over the next day), not deterministic predictions of exact events. No method currently predicts the precise time, place, and size of the next earthquake. The California test stands out for its scale (number of models, decade- long duration, and volume of prospective observations) compared to earlier CSEP efforts in Japan, New Zealand, and Italy.<a href=\"https:\/\/www.bristol.ac.uk\/news\/2026\/october\/new-forecasting-research-could-help-millions-living-in-earthquake-prone-regions-.html\" target=\"_blank\" rel=\"noopener\">\u2060<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The results provide strong evidence supporting the use of these model types in public OEF systems for situational awareness, especially during elevated activity periods. The team offers recommendations (\u201cmodel recipes\u201d), open- source software (including pyCSEP tools and reproducibility packages), and benchmarking data to aid development of operational systems worldwide. Funding came from the Leverhulme Trust, USGS, and Statewide California Earthquake Center.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>In short, the nearly 600 California earthquakes supplied rigorous, bias- resistant validation that certain next- day seismic risk models work as intended over long periods, bolstering confidence in their practical value for preparedness without overclaiming predictive power.<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"903\" data-attachment-id=\"479412\" data-permalink=\"https:\/\/climatescience.press\/?attachment_id=479412\" data-orig-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?fit=1974%2C2466&amp;ssl=1\" data-orig-size=\"1974,2466\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?fit=723%2C903&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=723%2C903&#038;ssl=1\" alt=\"A grid of earthquake prediction model visualizations displaying various versions of ETAS, STEP, and alternative models with corresponding parameters. Each model projection covers a specific geographic area in California, showcasing expected earthquake counts.\" class=\"wp-image-479412\" srcset=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=820%2C1024&amp;ssl=1 820w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=240%2C300&amp;ssl=1 240w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=768%2C959&amp;ssl=1 768w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=1230%2C1536&amp;ssl=1 1230w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=1639%2C2048&amp;ssl=1 1639w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?resize=640%2C800&amp;ssl=1 640w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?w=1974&amp;ssl=1 1974w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-98.png?w=1446&amp;ssl=1 1446w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><figcaption class=\"wp-element-caption\">Forecast maps generated by 21 <em>M<\/em><sub>w<\/sub>\u2009\u2265\u20093.95 next-day seismicity models and one time-invariant model (HKJ-ADAPTIVE-M2) for four different seismically calm days in California. Credit: <em>Nature Communications<\/em> (2026). DOI: 10.1038\/s41467-026-76243-7<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>ETAS (Epidemic-Type Aftershock Sequence) and STEP (Short- Term Earthquake Probability) are two leading statistical models used for short- term earthquake forecasting, particularly for aftershock sequences and clustered seismicity.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Both are widely applied in <strong>operational earthquake forecasting (OEF)<\/strong> systems by agencies such as the USGS, Italy\u2019s INGV, and New Zealand\u2019s Earth Sciences agencies. They rely on well- established empirical laws of seismicity rather than deterministic physical prediction of individual events.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Core Shared Principles<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Both models describe seismicity as a combination of:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A <strong>background<\/strong> (or spontaneous and long- term) rate of earthquakes.<\/li>\n\n\n\n<li><strong>Triggered<\/strong> (clustered) seismicity, where past earthquakes increase the likelihood of future ones.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>They incorporate classic empirical relations:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Omori- Utsu law:<\/strong> Aftershock rates decay as a power law with time after a triggering event (roughly rate<strong> \u221d 1\/(t + c)^p)<\/strong>.<\/li>\n\n\n\n<li><strong>Gutenberg- Richter law:<\/strong> Earthquake frequency decreases exponentially with magnitude (more small events than large ones).<\/li>\n\n\n\n<li><strong>Productivity scaling:<\/strong> Larger earthquakes trigger more aftershocks.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These enable probabilistic forecasts of the expected rate (or probability) of earthquakes of certain magnitudes in space and time, often for the next day, week, or other short windows, without claiming to predict exact times or locations of individual shocks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>ETAS Model<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Introduced by Yosihiko Ogata in the late 1980s, ETAS is a <strong>self- exciting point process<\/strong> (a type of Hawkes process). It treats every earthquake as a potential <strong>\u201cparent\u201d <\/strong>that can trigger its own <strong>\u201coffspring\u201d <\/strong>aftershocks, which can in turn trigger further events, forming a cascading, epidemic-like branching process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The conditional intensity (expected rate) at a given time <em>t<\/em>, location, and above a magnitude threshold is typically expressed as:<\/strong><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mi>\u03bb<\/mi><mo stretchy=\"false\">(<\/mo><mi>x<\/mi><mo separator=\"true\">,<\/mo><mi>y<\/mi><mo separator=\"true\">,<\/mo><mi>t<\/mi><mo>\u2223<\/mo><msub><mi>H<\/mi><mi>t<\/mi><\/msub><mo stretchy=\"false\">)<\/mo><mo>=<\/mo><mi>\u03bc<\/mi><mo stretchy=\"false\">(<\/mo><mi>x<\/mi><mo separator=\"true\">,<\/mo><mi>y<\/mi><mo stretchy=\"false\">)<\/mo><mo>+<\/mo><munder><mo>\u2211<\/mo><mrow><mi>i<\/mi><mo>:<\/mo><msub><mi>t<\/mi><mi>i<\/mi><\/msub><mo>&lt;<\/mo><mi>t<\/mi><\/mrow><\/munder><mi>K<\/mi><mo>\u22c5<\/mo><msup><mn>10<\/mn><mrow><mi>\u03b1<\/mi><mo stretchy=\"false\">(<\/mo><msub><mi>m<\/mi><mi>i<\/mi><\/msub><mo>\u2212<\/mo><msub><mi>m<\/mi><mi>c<\/mi><\/msub><mo stretchy=\"false\">)<\/mo><\/mrow><\/msup><mo>\u22c5<\/mo><mi>\u03bd<\/mi><mo stretchy=\"false\">(<\/mo><mi>t<\/mi><mo>\u2212<\/mo><msub><mi>t<\/mi><mi>i<\/mi><\/msub><mo stretchy=\"false\">)<\/mo><mo>\u22c5<\/mo><mi>s<\/mi><mo stretchy=\"false\">(<\/mo><mi>x<\/mi><mo>\u2212<\/mo><msub><mi>x<\/mi><mi>i<\/mi><\/msub><mo separator=\"true\">,<\/mo><mi>y<\/mi><mo>\u2212<\/mo><msub><mi>y<\/mi><mi>i<\/mi><\/msub><mo stretchy=\"false\">)<\/mo><\/mrow><\/semantics><\/math><strong>Where:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>\u03bc<\/mi><mo stretchy=\"false\">(<\/mo><mi>x<\/mi><mo separator=\"true\">,<\/mo><mi>y<\/mi><mo stretchy=\"false\">)<\/mo><\/mrow><\/semantics><\/math> is the background rate (spatially varying, often from long-term seismicity).<\/li>\n\n\n\n<li>The sum is over past events <em>i<\/em>.<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>K<\/mi><\/mrow><\/semantics><\/math><em>K<\/em> and <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>\u03b1<\/mi><\/mrow><\/semantics><\/math><em>\u03b1<\/em> control productivity (how many aftershocks a magnitude <math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><msub><mi>m<\/mi><mi>i<\/mi><\/msub><\/mrow><\/semantics><\/math><em>m<\/em><em>i<\/em>\u200b event produces).<\/li>\n\n\n\n<li><em>\u03bd<\/em>(<em>t<\/em>&#8211; <em>ti<\/em>\u200b) is the temporal decay (Omori- Utsu form).<\/li>\n\n\n\n<li><em>s<\/em>(\u22c5) is a spatial kernel (often isotropic or anisotropic, decaying with distance).<\/li>\n\n\n\n<li><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><msub><mi>m<\/mi><mi>c<\/mi><\/msub><\/mrow><\/semantics><\/math> is a completeness magnitude threshold.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Key features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Fully recursive:<\/strong> secondary and higher- order aftershocks are naturally included.<\/li>\n\n\n\n<li>Parameters are usually estimated by maximum likelihood from a catalog (sometimes Bayesian methods are used for robustness).<\/li>\n\n\n\n<li><strong>Flexible spatial kernels and variants exist<\/strong> (e.g., adaptive smoothing, incorporation of smaller events, or slow- slip effects).<\/li>\n\n\n\n<li><strong>Strengths:<\/strong> Captures cascading triggering well; widely tested and extended; strong theoretical foundation.<\/li>\n\n\n\n<li><strong>Limitations:<\/strong> Can be computationally intensive; sensitive to catalog incompleteness; spatial kernels are often simplified (isotropic by default).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">ETAS and its variants performed well in the long- term California CSEP tests referenced in recent studies.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>STEP Model<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Developed by Gerstenberger and colleagues (around 2004- 2005), STEP builds on the simpler Reasenberg- Jones (1989) aftershock model and was designed for practical, real- time use (including early USGS aftershock forecasts).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>It also has background and clustered components, but differs in structure:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Background:<\/strong> Often taken from a long- term seismic hazard model (e.g., Frankel et al. smoothed seismicity or national hazard maps).<\/li>\n\n\n\n<li><strong>Clustering:<\/strong> Uses the Reasenberg- Jones formulation for aftershock rates after a mainshock, extended to multiple sequences. Parameters can be generic (from past sequences in similar settings) or sequence- specific (updated as new events occur).<\/li>\n\n\n\n<li><strong>Combination rule:<\/strong> Rates from background and aftershock components are typically combined by taking the maximum (rather than summing), which helps avoid double- counting in active areas.<\/li>\n\n\n\n<li><strong>Spatial component:<\/strong> Empirically derived decay with distance; earthquakes are classified into primary and secondary events or clusters, with zone- specific adjustments.<\/li>\n\n\n\n<li>Variants such as STEPJAVA (a Java reimplementation) appear in CSEP experiments.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Key features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>More modular and heuristic:<\/strong> Combines distinct model pieces rather than a single unified branching process.<\/li>\n\n\n\n<li>Computationally <strong>efficient and analytic in many implementations<\/strong>.<\/li>\n\n\n\n<li>Easily integrates with official <strong>long- term hazard maps<\/strong> for consistency.<\/li>\n\n\n\n<li><strong>Strengths:<\/strong> Practical for operational use; adapts parameters per sequence; often produces focused <strong>\u201chot spots\u201d<\/strong>.<\/li>\n\n\n\n<li><strong>Limitations:<\/strong> Less fully recursive than classic ETAS in some formulations; spatial rules can be more empirical.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Main Differences<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Aspect<\/th><th class=\"has-text-align-left\" data-align=\"left\">ETAS<\/th><th class=\"has-text-align-left\" data-align=\"left\">STEP<\/th><\/tr><\/thead><tbody><tr><td>Triggering style<\/td><td>Fully recursive branching (every event can trigger others equally)<\/td><td>Cluster and sequence- oriented; primary vs. secondary events<\/td><\/tr><tr><td>Rate combination<\/td><td>Background and sum of all triggers<\/td><td>Often max(background, clustered rates)<\/td><\/tr><tr><td>Parameter handling<\/td><td>Global or regionally fitted; fixed during forecast windows<\/td><td>Generic and sequence- specific; updated as events accrue<\/td><\/tr><tr><td>Spatial treatment<\/td><td>Continuous kernels<\/td><td>Empirical zones and distance decay<\/td><\/tr><tr><td>Computational style<\/td><td>Point-process intensity<\/td><td>More modular, often faster for real- time use<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">In prospective tests (including California CSEP experiments spanning ~2007- 2018), select versions of both models showed good consistency with observed numbers, locations, and magnitudes of moderate earthquakes, supporting their use in public OEF. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Performance is often comparable, with results depending on the specific variant, catalog, and evaluation metric; neither perfectly forecasts the occurrence of large mainshocks themselves (a known limitation of pure clustering models).<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Practical Use<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Both generate probabilistic rate maps or expected counts for short time windows. They are routinely updated with new catalog data. Modern research continues to refine them (e.g., better handling of incompleteness, anisotropy, swarms, or hybrid deep- learning combinations).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In short, <strong>ETAS<\/strong> emphasizes a unified, cascading statistical process, while<strong> STEP <\/strong>emphasizes practical, adaptive combination of background hazard and aftershock sequences. Together they form the backbone of many current short- term seismic risk forecasts.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"575\" data-attachment-id=\"479480\" data-permalink=\"https:\/\/climatescience.press\/?attachment_id=479480\" data-orig-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?fit=979%2C779&amp;ssl=1\" data-orig-size=\"979,779\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?fit=723%2C575&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?resize=723%2C575&#038;ssl=1\" alt=\"Map of significant earthquakes in California and surrounding areas from 2008 to 2018, displaying earthquake locations with magnitudes indicated by circle sizes and colors representing different years.\" class=\"wp-image-479480\" srcset=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?w=979&amp;ssl=1 979w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?resize=300%2C239&amp;ssl=1 300w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?resize=768%2C611&amp;ssl=1 768w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-99.png?resize=640%2C509&amp;ssl=1 640w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><figcaption class=\"wp-element-caption\">Image shows spatial earthquake forecasts generated by the models for specific days. Bright colours denote regions of relatively high expected seismicity, while dark regions indicate the opposite.<br>Image credit: J. Bayona<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-large-font-size wp-block-paragraph\"><strong>Select earthquake forecasting models demonstrate consistency with prospective decadal observations in California<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Abstract Summary<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Earthquake forecasting systems are operational in several countries. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To validate them rigorously, the authors prospectively tested <strong>27 forecasting models<\/strong> (including types used for operational earthquake forecasting) operated by the Collaboratory for the Study of Earthquake Predictability (CSEP). <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">From August 2007 to August 2018 these models produced 51,625 next- day Mw \u2265 3.95 seismicity forecasts for California. The forecasts were evaluated against 597 observed Mw \u2265 3.95 earthquakes (depths \u2264 30 km) using community- vetted statistical methods.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Background and Methods<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">CSEP ran fully automated, prospective experiments to avoid retrospective bias. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Models included variants of:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Epidemic- Type Aftershock Sequence (ETAS)<\/li>\n\n\n\n<li>Short- Term Earthquake Probability (STEP)<\/li>\n\n\n\n<li>Alternatives (e.g., PPE, EEPAS)<\/li>\n\n\n\n<li>Nonparametric and ensemble models<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Forecasts were issued daily at midnight UTC for the next 24 hours on a spatial grid, using only prior data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"> Evaluation used CSEP tests for number (Poisson and Negative Binomial Distribution), spatial distribution, and magnitude distribution, plus comparisons against a time- invariant smoothed seismicity benchmark (HKJ-ADAPTIVE-M2).  <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Focus was primarily on the 21 models targeting Mw \u2265 3.95 (results for a smaller set of Mw \u2265 4.95 models appear in supplementary material). The catalog included sequences such as the 2010 Mw 7.2 El Mayor\u2013Cucapah, 2012 Brawley swarm, and 2014 Mw 6.0 South Napa.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Key Results Highlights<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Number consistency:<\/strong> Nearly all models (20\/21 under the NBD test) matched cumulative observed counts statistically, despite fixed productivity parameters and fully automated operation. About half differed by \u226420% from observations; strong performers included STEPJAVA and certain ETAS versions.<\/li>\n\n\n\n<li><strong>Daily performance:<\/strong> Models often under -predicted rates on the day of large mainshocks (inherent limitation of clustering models) but performed better in subsequent days or quiet periods.<\/li>\n\n\n\n<li><strong>Spatial and magnitude:<\/strong> Over two- thirds consistent spatially; 95% matched magnitude distributions. Nonparametric and adaptive- smoothing components helped spatial skill.<\/li>\n\n\n\n<li><strong>Overall, time- varying clustering models<\/strong> added value over purely time- invariant ones. Newer models and those using smaller earthquakes as inputs tended to perform relatively better.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Discussion and Implications<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The study supports use of ETAS- and STEP- type models in public OEF for situational awareness.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Limitations remain (e.g., forecasting the exact occurrence of large events themselves and handling certain swarms). The authors offer <strong>\u201cmodel recipes\u201d<\/strong>, open- source reproducibility tools (Code Ocean capsule and GitHub), forecast data via CSEP, and the USGS earthquake catalog.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Data and Code Availability:<\/strong> Forecasts at cseptesting.org; catalog via USGS; software packages cited in the paper (including a peer- reviewed Code Ocean version).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This paper provides one of the most extensive prospective validations of next- day seismic forecasting models to date and underpins recent coverage about nearly 600 California earthquakes validating such models. The full article (including figures, methods, and supplementary material) is freely available at the linked URL.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Journal:<\/strong> <a href=\"https:\/\/phys.org\/journals\/nature-communications\/\">Nature Communications<\/a>, (volume <strong>17<\/strong>, Article number: 9444<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Published<\/strong>: 06 August 2026, <a href=\"https:\/\/www.springernature.com\/gp\/open-science\/about\/the-fundamentals-of-open-access-and-open-research\">Open access<\/a> paper<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>DOI:<\/strong> <a href=\"https:\/\/dx.doi.org\/10.1038\/s41467-026-76243-7\" target=\"_blank\" rel=\"noopener\">10.1038\/s41467-026-76243-7<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Provided:<\/strong> <a href=\"https:\/\/phys.org\/partners\/university-of-bristol\/\">University of Bristol<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Authors:<\/strong> <a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Jos__A_-Bayona-Aff1\">Jos\u00e9 A. Bayona<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Francesco-Serafini-Aff1-Aff6\">Francesco Serafini<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-F_bio-Silva-Aff2\">F\u00e1bio Silva<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Pablo-Iturrieta-Aff3\">Pablo Iturrieta<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-William_H_-Savran-Aff4\">William H. Savran<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Marcus-Herrmann-Aff5\">Marcus Herrmann<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Warner-Marzocchi-Aff5\">Warner Marzocchi<\/a>,<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Philip_J_-Maechling-Aff2\">Philip J. Maechling<\/a> &amp;<br><a href=\"https:\/\/www.nature.com\/articles\/s41467-026-76243-7#auth-Maximilian_J_-Werner-Aff1\">Maximilian J. Werner<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"723\" height=\"482\" data-attachment-id=\"479491\" data-permalink=\"https:\/\/climatescience.press\/?attachment_id=479491\" data-orig-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?fit=1280%2C852&amp;ssl=1\" data-orig-size=\"1280,852\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?fit=723%2C482&amp;ssl=1\" src=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?resize=723%2C482&#038;ssl=1\" alt=\"Illustration of the Earth with a seismic wave graph and red concentric circles representing an earthquake epicenter.\" class=\"wp-image-479491\" srcset=\"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?resize=1024%2C682&amp;ssl=1 1024w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?resize=300%2C200&amp;ssl=1 300w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?resize=768%2C511&amp;ssl=1 768w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?resize=640%2C426&amp;ssl=1 640w, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/image-100.png?w=1280&amp;ssl=1 1280w\" sizes=\"auto, (max-width: 723px) 100vw, 723px\" \/><figcaption class=\"wp-element-caption\">The findings could have practical implications for government agencies, civil protection authorities and scientific organisations developing &#8211; or considering developing &#8211; operational earthquake forecasting systems.<br>Image credit: Tumisu<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A decade- long prospective evaluation of earthquake forecasting models in California, tested against nearly 600 quakes (exactly 597 of magnitude Mw \u2265 3.95), shows that select models, especially certain versions of well- established operational ones, can reliably describe next- day patterns of earthquake numbers, locations, and magnitudes.<\/p>\n<p>ETAS (Epidemic-Type Aftershock Sequence) and STEP (Short- Term Earthquake Probability) are two leading statistical models used for short- term earthquake forecasting, particularly for aftershock sequences and clustered seismicity.<\/p>\n","protected":false},"author":121246920,"featured_media":479396,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","advanced_seo_description":"Discover how nearly 600 California earthquakes validate next-day forecasting models, enhancing preparedness for seismic risks effectively.","jetpack_seo_html_title":"California Study Validates Next-Day Earthquake Forecasting Models","jetpack_seo_noindex":false,"jetpack_seo_schema_type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691846422,691846420,691846416,691846421,691846417,691846418,691846423,691828870],"class_list":["post-479395","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-27-forecasting-models","tag-600-california-earthquakes","tag-collaboratory-for-the-study-of-earthquake-predictability-csep","tag-earthquake-prediction-and-forecasting-models","tag-epidemic-type-aftershock-sequence-etas","tag-short-term-earthquake-probability-step","tag-statewide-california-earthquake-center","tag-university-of-bristol","fallback-thumbnail"],"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-20Ib","jetpack-related-posts":[{"id":273704,"url":"https:\/\/climatescience.press\/?p=273704","url_meta":{"origin":479395,"position":0},"title":"No, \u2018The Conversation,\u2019 Climate Change Isn\u2019t Increasing Earthquakes","author":"uwe.roland.gross","date":"08\/15\/2023","format":false,"excerpt":"A story posted in The Conversation, and\u00a0distributed by MSN News, claims that climate change is influencing the number of earthquakes. Data shows this is false. The study was so limited in scope it isn\u2019t representative of climate change either by reach or time.","rel":"","context":"In \"Climate change\"","block_context":{"text":"Climate change","link":"https:\/\/climatescience.press\/?tag=climate-change"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/08\/image-624.png?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/08\/image-624.png?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/08\/image-624.png?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/08\/image-624.png?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/08\/image-624.png?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":238168,"url":"https:\/\/climatescience.press\/?p=238168","url_meta":{"origin":479395,"position":1},"title":"Green EU: The Massive Gas Field Nobody will Touch","author":"uwe.roland.gross","date":"01\/06\/2023","format":false,"excerpt":"Fear of earthquakes is preventing Netherlands from pumping gas from a reservoir big enough to insulate Europe from the consequences of their green energy policy failure and Russian energy geopolitics.","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/01\/image-226.png?fit=1152%2C769&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/01\/image-226.png?fit=1152%2C769&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/01\/image-226.png?fit=1152%2C769&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/01\/image-226.png?fit=1152%2C769&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/01\/image-226.png?fit=1152%2C769&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":475889,"url":"https:\/\/climatescience.press\/?p=475889","url_meta":{"origin":479395,"position":2},"title":"The 2025 Kamchatka Tsunami Kept the Pacific Ringing for Hours \u2014 Its Rhythm Could Improve Tsunami Warnings","author":"uwe.roland.gross","date":"10\/02\/2026","format":false,"excerpt":"The 2025 Kamchatka tsunami (from the Mw 8.8 megathrust earthquake on 29 July 2025 off the Kamchatka Peninsula) produced long- lasting, coherent oscillations across the Pacific that persisted for many hours, and analysis of its dominant periods (its \u201crhythm\u201d) has potential value for improving tsunami warnings. DART stands for Deep-\u2026","rel":"","context":"In \"2025 Kamchatka tsunami\"","block_context":{"text":"2025 Kamchatka tsunami","link":"https:\/\/climatescience.press\/?tag=2025-kamchatka-tsunami"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-The-2025-Kamchatka-Tsunami-Kept-the-Pacific-Ringing-for-Hours-%E2%80%94-Its-Rhythm-Could-Improve-Tsunami-Warnings.jpg?fit=1168%2C784&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-The-2025-Kamchatka-Tsunami-Kept-the-Pacific-Ringing-for-Hours-%E2%80%94-Its-Rhythm-Could-Improve-Tsunami-Warnings.jpg?fit=1168%2C784&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/10\/0-The-2025-Kamchatka-Tsunami-Kept-the-Pacific-Ringing-for-Hours-%E2%80%94-Its-Rhythm-Could-Improve-Tsunami-Warnings.jpg?fit=1168%2C784&ssl=1&resize=525%2C300 1.5x, 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Megathrust Earthquake","link":"https:\/\/climatescience.press\/?tag=1700-cascadia-megathrust-earthquake"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0AQNQOPQ1WJiPiujYmTBtlv1p1W-O7zfK5dC5u46KWK0DZujZOynXu3az-hG5QWJKWPSYls5Nr8w0A-I_oAuIcFdg_5HUy21v2LzW4PbJRo5EShk-Yj_u4n6DiplY9_pq-1.jpeg?fit=1200%2C594&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0AQNQOPQ1WJiPiujYmTBtlv1p1W-O7zfK5dC5u46KWK0DZujZOynXu3az-hG5QWJKWPSYls5Nr8w0A-I_oAuIcFdg_5HUy21v2LzW4PbJRo5EShk-Yj_u4n6DiplY9_pq-1.jpeg?fit=1200%2C594&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0AQNQOPQ1WJiPiujYmTBtlv1p1W-O7zfK5dC5u46KWK0DZujZOynXu3az-hG5QWJKWPSYls5Nr8w0A-I_oAuIcFdg_5HUy21v2LzW4PbJRo5EShk-Yj_u4n6DiplY9_pq-1.jpeg?fit=1200%2C594&ssl=1&resize=525%2C300 1.5x, 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radiation\"","block_context":{"text":"Cosmic radiation","link":"https:\/\/climatescience.press\/?tag=cosmic-radiation"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00chirality-molecules-cosmic-rays_1600.jpg?fit=1200%2C686&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00chirality-molecules-cosmic-rays_1600.jpg?fit=1200%2C686&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00chirality-molecules-cosmic-rays_1600.jpg?fit=1200%2C686&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00chirality-molecules-cosmic-rays_1600.jpg?fit=1200%2C686&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/00chirality-molecules-cosmic-rays_1600.jpg?fit=1200%2C686&ssl=1&resize=1050%2C600 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